online purchase intention literature review

ORIGINAL RESEARCH article

Enhancing consumer online purchase intention through gamification in china: perspective of cognitive evaluation theory.

• 1 Business School, Yango University, Fuzhou, China
• 2 School of Economics, Fujian Normal University, Fuzhou, China
• 3 School of Economics and Management, Foshan University, Foshan, China
• 4 School of Public Administration, Xi’an University of Architecture and Technology, Xi’an, China

The application of game elements of gamification in online shopping is attracting interest from researchers and practitioners. However, it remains unclear how gamification affects and improves consumer purchase intention on online shopping platforms, which still leaves a gap in our knowledge. To narrow this theoretical gap, a theoretical model has been built in this study. This model adopts cognitive evaluation theory to explain the impact of gamification elements on consumer purchase intention. Data was collected from 322 online shopping consumers who used a flash game to test their purchase intention after playing games. The results show that game rewards, absorption and autonomy of gamification positively enhance sense of enjoyment, and that it helps people meet their psychological needs, which ultimately affects the online purchase intention of consumers. This study is helpful in analyzing the factors involved in the successful introduction of gamification on online shopping platforms in more detail.

Introduction

As mobile applications and social media have evolved, competition in the online shopping market has grown fiercer, with many businesses working to affect consumer behavior ( Wang and Fesenmaier, 2003 ). An increasing number of businesses are competing for a share of the market by attracting active consumers. As a relatively new paradigm for engaging people, gamification is applied as a strategy to influence and motivate people to participate in education, marketing, training, networking, and health-related activities ( Bunchball, 2010 ). Gamification is the implementation of dynamic components and elements of games ( Zichermann and Linder, 2010 ; Mullins and Sabherwal, 2020 ) that are not directly related to games ( Bunchball, 2010 ) and appear in non-game contexts ( Deterding et al., 2011 ). The term “gamification” was first used in 2002, but it was not until 2010 that this concept of gamification became popular ( Mitchell et al., 2020 ).

Introducing game mechanics into business is the science of enriching consumer interaction, while games for commercial purposes are still under development ( Zichermann and Cunningham, 2011 ). In this sense, it is urgent for platforms to learn how to introduce game mechanisms into their business to provide their consumers with a rewarding, enjoyable, and fun experience. As an emerging way to attract consumers, gamification is being used in marketing, school education and training, on the Internet, and in related industries ( Huang and Cappel, 2005 ; Silverman, 2011 ; Hordemann and Chao, 2012 ; Kankanhalli et al., 2012 ). In this context, the design of game elements, such as inspiration, competition mechanisms, and shock, is used to increase the value of high enjoyment to attract consumers ( Simões et al., 2013 ; Seaborn and Fels, 2015 ; Müller-Stewens et al., 2017 ; Mullins and Sabherwal, 2020 ).

Taking this context into account, gamification has been clearly deemed as a means of driving consumer behavior. Gamification is the utilization of game design elements in non-game contexts ( Deterding et al., 2011 ; Mitchell et al., 2020 ). Since 2002, gamification ( Hamari and Lehdonvirta, 2010 ; Deterding et al., 2011 ) and persuasive technologies ( Fogg, 2002 ) have been harnessed for business purposes and to influence customer behavior. The control of game elements in gamification may have a positive impact on the experience of playing games and the generation of customers’ intention ( Poncin et al., 2017 ; Mitchell et al., 2020 ; Mullins and Sabherwal, 2020 ). For instance, Alibaba has set up a game mechanism on its payment platform, on which the quantity of trees planted depends on individual walks, so as to fulfill its social responsibility and stimulate consumption through the platform. On the other hand, as gamification is heavily driven by information communication technologies, it is natural to address interrelations between gamification and online behavior of consumers ( Huang et al., 2017 ). For example, JD.com , a large online shopping platform in China, enables people to gain points, known as beans, when they make purchases; these beans can then be exchanged for other commodities or planted on the game platform in order to obtain more beans and increase consumer willingness on this platform.

Although there has been a lot of research on online consumer behavior ( Chen et al., 2015 ), there is a lack of research on gamification from the perspective of consumer behavior ( Sigala, 2015 ). In the context of fierce competition among online shopping platforms, many such platforms not only face domestic competitors, but also have to consolidate the barriers to entry of foreign competitors ( Xi and Hamari, 2020 ). Thus, the concept of gamification is an important source of stimulation in the marketing theory of consumer behavior decision ( Tobon et al., 2020 ; Xi and Hamari, 2020 ), and it provides specific directions for researchers in the study of online marketing. Therefore, this study aims to explore the effect of gamification on consumers’ online purchase intention.

For this purpose, a theoretical model has been developed to predict the impact of consumers’ enjoyment in the game on their purchase intention by drawing on cognitive evaluation theory (CET) ( Ryan and Deci, 2000a , b ; Deci and Ryan, 2010 ; Mitchell et al., 2020 ). According to CET, when people are involved in certain activities, they have psychological needs such as autonomy and absorption. When individuals feel that their demands need to be met, they will trigger intrinsic motivation and feel a greater sense of enjoyment, which, in turn, will lead to more engagement in activities ( Lee and Yang, 2011 ) and ultimately affect consumer behavior. Since the main purpose of gamification is to develop willpower and high-quality forms of motivation, CET helps us understand the changes in consumer behavior in the context of gamification. Based on CET, a model has been developed and tested in this study to explore how game elements affect users’ psychological needs and increase consumers’ sense of enjoyment, thereby influencing their purchase intention.

According to the literature on meaning ( Webster and Ahuja, 2006 ; Sen et al., 2008 ; Seaborn and Fels, 2015 ), people derive meaning when their activities are consistent with core aspects of enjoyment. Autonomy, rewards, and absorption are important factors for the success of gamification ( Sigala, 2015 ; Mitchell et al., 2020 ), as well as lying at the core of CET. According to the above explanations, this study intends to propose relevant research contributions on the basis of the following theoretical gaps: (1) applying CET to explore the important role of gamification in consumer online purchase intention; (2) focusing on verifying characterized game elements of gamification, which is conducive to filling the gap of variable measurement in the theoretical literature on CET; (3) enriching applications of gamification for business and academics, particularly those that add new features and gameplay mechanics ( Huotari and Hamari, 2017 ) to ensure both customer enjoyment and the success of business objectives.

Literature Review and Theory Development

Cognitive evaluation theory.

Cognitive evaluation theory is a psychological theory that aims to explain the effect of extrinsic results on intrinsic motivation. CET proposes the concept of “intrinsic incentive,” which is also known as “intrinsic motivation.” The theory suggests that people are more likely to participate in an activity when they have intrinsic motivations such as an experience of enjoyment ( Agarwal and Karahanna, 2000 ; Gottschalg and Zollo, 2007 ; Beecham et al., 2008 ). Deci and Ryan (2000) proposed three types of motivation: extrinsic regulation, intrinsic regulation, and intrinsic motivation. Their study emphasized that motivation needs to be intrinsic rather than extrinsic. The central focus of Deci and Ryan’s research was on intrinsic motivation and the antecedents that increase persistence. They defined intrinsic motivation as performing an activity solely for inherent satisfaction. This is a broader view that people motivated intrinsically are more stimulated and perform better than others ( Cerasoli et al., 2014 ). Although researchers regard intrinsic motivation as an inherent quality, the maintenance and enhancement of this motivation depends on the social and environmental conditions around the individual. Deci and Ryan’s CET proposed that individuals’ significant psychological needs are satisfied when the individuals perceive that they can regulate their behaviors. Intrinsic motivation is supported by social and environmental factors, such as events and conditions, that enhance an individual’s sense of autonomy and competence, whereas it is undermined by factors that diminish perceived autonomy or competence ( Deci and Ryan, 1980 ; Chae et al., 2017 ). Withdrawing on theoretical foundation, this study adopts CET to build conceptual framework of gamification and expands upon how gamification elements are key determinants of consumer enjoyment, intrinsically motivated purchase intention.

By extending such aspects of CET to this study, it is possible to consider the behaviors of extrinsic regulation to be motivated by external factors such as awards and competition, and the behaviors of intrinsic regulation to be motivated by internal factors such as absorption and autonomy. When an individual realizes that the causation originates from the behaviors mentioned above, intrinsic motivation appears. An example of intrinsic motivation is enjoyment. When people are dominated by intrinsic motivation, they will stick to a task for longer and like it more ( Deci, 1985 ). The contribution of CET is that it proposes the factors that enable people to generate intrinsic motivation, which are specifically autonomy and competence. Autonomy means the willpower or willingness to do a task; competence refers to the feeling of being effective ( Silverman, 2011 ; Santhanam et al., 2016 ; Huang et al., 2017 ), such as getting rewards, being addicted to games, and participating in competition.

Consumer Enjoyment

Consumer enjoyment is “a necessary response of humans to activities with computers as intermediaries” ( Laurel, 2013 ). When consumers are attracted by a game, a sense of enjoyment will be generated ( Jacques, 1995 ). Intrinsic motivation of expected enjoyment derives from the pleasure or inherent interest in doing something ( Gagné and Deci, 2005 ). Curiosity, fun, or enjoyment can all be intrinsic motivations ( Kim and Drumwright, 2016 ). Based on CET, intrinsic motivation derives from one’s preference for an activity. People will gain inherent satisfaction from doing it, intrinsic motivation reflects the desire to engage in a task for its enjoyment ( Tao and Yun, 2019 ). Enjoyment of an activity is generally viewed as an important intrinsic motivation ( Kim and Drumwright, 2016 ; Hew et al., 2018 ). Consumer enjoyment is important because it allows people to have a positive outlook on human–computer interaction, thus increasing future motivation for repeated interactions with games ( Kim and Moon, 1998 ; Webster and Ahuja, 2006 ). This, in turn, leads to the success of a game ( Hwang and Thorn, 1999 ).

Studies have shown that consumer enjoyment can develop positive attitudes through certain activities, such as gaining rewards, absorption in games, participation in competition, and feeling self-control ( Schaufeli et al., 2002 ). These subdimensions represent the emotional, cognitive, and physical aspects of consumer enjoyment ( Chen et al., 2015 ). In this study, autonomy is defined as the voluntary participation of a consumer in an activity designed by gamification and the consumer’s continuous efforts to gain rewards in the face of difficulties. Competition comprises the senses of meaning, pride, and challenge, as well as the inspiration and passion of consumers. Enjoyment refers to the extent to which a consumer’s experience culminates in pleasure and excitement triggered by the online gamified environment. Some scholars hold that some psychological needs should be satisfied if people want to keep their intrinsic motivation (i.e., enjoyment) ( Ryan et al., 2006 ). In other words, when a person’s basic psychological needs of competence, autonomy, and relevance are satisfied by an activity, greater enjoyment will be gained.

In this case, enjoyment is the extent to which an individual obtains a pleasant experience while playing games ( Huotari and Hamari, 2017 ). CET predicts that if people consider an activity involving a certain form of technology to be enjoyable, the intrinsic motivation will be increased and extrinsic behaviors will ultimately be affected ( O’Brien, 2010 ; Lee and Yang, 2011 ). In the field of online shopping, enjoyment is considered to be a motivational state that can influence the degree and focus of consumption ( Bunchball, 2010 ). Purchase intention is defined as a spontaneous and powerful shopping tendency and a shopping process that is dominated by consumers themselves ( Rook and Fisher, 1995 ). In a state of enjoyment, consumers tend to feel environmental stimuli and arousal impulses ( Wang and Li, 2016 ). As the purpose of gamification is mainly to make consumers’ activities more enjoyable ( Bunchball, 2010 ), enjoyment is a significant intrinsic motivation that determines whether consumers participate in designed gamified shopping environment and affects purchase intention. On this basis, we propose the following hypothesis:

H1: Consumer enjoyment has a positive impact on online purchase intention.

Gamification

Gamification can collect user data for salespeople to observe user preference ( Nelson, 2005 ). If users develop a negative attitude toward the instrumental trait of a certain game, they will not play the game anymore, which hinders the development of a favorable brand attitude and game skills ( Kwak et al., 2012 ; Xi and Hamari, 2020 ). Some scholars have suggested conducting a survey on specified gamification design elements, so as to improve the design and obtain the benefits of gamification ( Kim and Johnson, 2016 ; Mitchell et al., 2020 ; Mullins and Sabherwal, 2020 ). Thus, it is quite important to analyze the use of gamification business applications to understand the impact of gamification and social cognition on e-commerce success ( Wakefield et al., 2011 ; Tobon et al., 2020 ; Xi and Hamari, 2020 ). According to CET, increasing all aspects of value can enhance more customers’ experience of enjoyment and ultimately promote online consumer behavior.

Gamification can serve to enhance consumer enjoyment with online shopping ( Huotari and Hamari, 2017 ). The factors that stimulate consumer online shopping are closely associated with the motivation to participate in games and can be divided into two categories: intrinsic motivation and extrinsic motivation. Both extrinsic and intrinsic motivation play a significant role in online shopping. However, according to CET, intrinsic motivation represents enjoyment in an activity for its own sake ( Mekler et al., 2017 ). For example, people who shop online because they enjoy looking over new things and expanding their consumer knowledge are intrinsically motivated to be there. However, some scholars agree that the intrinsic motivation factor is more important than extrinsic motivation and has a greater impact on consumer behavior ( Reiss, 2004 ; Deterding et al., 2011 ; Tobon et al., 2020 ).

Moreover, merely adding gamification mechanics such as challenge and fantasy in a smart interface is not enough to significantly enhance the quality of the perceived experience ( Insley and Nunan, 2014 ; Mitchell et al., 2020 ). The purpose of gamification is to increase consumer motivation and facilitate consumers’ participation in gamification activities through intrinsic and extrinsic motivators, and to provide a pleasant experience ( Von Ahn and Dabbish, 2008 ; Conaway and Garay, 2014 ; Xi and Hamari, 2020 ). Reward, competition, autonomy, and absorption are the most common game dynamics in the literature on gamification ( Agarwal and Karahanna, 2000 ; Gottschalg and Zollo, 2007 ; Liu et al., 2007 ; Hordemann and Chao, 2012 ; Kapp, 2012 ; Mullins and Sabherwal, 2020 ), these elements must be available in order for gamification to be used ( Conaway and Garay, 2014 ). As a result, consumers are encouraged to further participate in the system ( Gottschalg and Zollo, 2007 ), which ultimately affects purchase intention. Furthermore, a gamified campaign needs to be well executed in order to achieve the intended goals ( Lucassen and Jansen, 2014 ). To represent components of gamification specifically, reward, competition, autonomy, and absorption have been adopted as measurement dimensions of gamification in this study ( Agarwal and Karahanna, 2000 ; Gottschalg and Zollo, 2007 ; Liu et al., 2007 ; Hordemann and Chao, 2012 ; Kapp, 2012 ), and Table 1 summarizes the definitions of these dimensions.

Table 1. Definitions of variables in gamification.

Based on CET, researchers hold that consumer competence is an important prerequisite for triggering enjoyment. When a consumer feels that he/she is controlled or forced to do something (e.g., participate in an unpleasant competitive relationship), any external condition will reduce the intrinsic motivation and lessen the experience of enjoyment ( Antin and Churchill, 2011 ). Players’ voluntary enjoyment is the key element of a game ( Huotari and Hamari, 2017 ).

Playing a game means the player is in an environment where he/she has autonomy ( Gagné and Deci, 2005 ), and people participate in the game of their own free will. This is an exact reflection of autonomy. Game activities, such as completing tasks, defeating other players, and developing strategies to achieve goals with other players, can help people meet their psychological needs of autonomy, competence, and relevance ( Deci and Ryan, 2000 ; Beecham et al., 2008 ; Mitchell et al., 2020 ), and improve the inner experience of enjoyment. According to CET, people have more fun when engaging in activities in which they are interested or in activities that can reflect their personal value ( Ryan et al., 2006 ). When external conditions are able to meet internal psychological needs, external factors can increase the intrinsic motivation and enable people to experience enjoyment ( Antin and Churchill, 2011 ; Mitchell et al., 2020 ). In other words, the greater the freedom perceived by consumers when making orders on an online shopping platform, the greater the efficiency in triggering the consumers’ intrinsic motivation to engage in the consumption process and in further satisfying their psychological needs ( Rogers, 2017 ). From this logic, we can infer that when gamification is applied in the context of online shopping, enjoyment can be more easily triggered if the need for autonomy is satisfied. Based on the above discussion, we have developed the following hypothesis:

H2: Autonomy of gamification has a positive impact on enjoyment.

In the design of gamification, rewards are what the user receives as a return for completing pre-assigned tasks. Rewards and challenges have been identified as the two mechanisms that are most commonly used for gamification ( Tobon et al., 2020 ). Rewards can motivate consumers to make every effort to improve their level and get more points or loots ( Deterding et al., 2011 ). CET confirms the importance of rewards. Players can earn points, rise to a higher level, or get badges or discounts as rewards ( Hofacker et al., 2016 ). People are motivated to gain more rewards. For example, the ranking place on the leaderboard can stimulate a player’s desire to compete with others for better scores ( Hordemann and Chao, 2012 ). These reward mechanisms are helpful in intensifying the intrinsic motivation to get a better experience of enjoyment ( Przybylski et al., 2010 ). Thus, by helping people to meet their psychological needs, rewards can stimulate people’s intrinsic motivation to get a better experience of enjoyment from specific activities.

According to CET, obtaining real returns through gamification can enhance the consumer experience and help consumers achieve higher satisfaction ( Deci and Ryan, 2000 ). Moreover, some scholars believe that rewards can bring a higher level of enjoyment ( Johnson et al., 2018 ). Through the continuous accumulation of points, consumers have confidence in their own capability, which can then improve their sense of enjoyment ( Francisco-Aparicio et al., 2013 ). Consumers can also exchange points earned from rewards with virtual discounts or products according to their own needs. For example, consumers are rewarded for reaching higher levels, which gives them a sense of achievement and allows them to feel self-worth. Hence, the more rewards consumers gain through gamification, the more they consider themselves valuable ( Przybylski et al., 2010 ) and the easier it is to generate enjoyment. Thus, we propose our third hypothesis:

H3: Rewards of gamification have a positive impact on the generation of enjoyment.

Absorption in gamification has a strong influence on individual behavior change ( Silic and Lowry, 2020 ). According to CET, people can count on intrinsic motivation to generate stable actions when they are immersed in their own world ( Rook and Fisher, 1995 ). For consumers using gamification, absorption is a state of enjoyment. Under this state, players can be absorbed in these games. This can be seen as a process of high enjoyment. Gamification allows players to immerse themselves in a virtual world, helping them escape from some of the problems in the real world. Some players may be absorbed in a game, enjoy mental relaxation, and feel that time passes faster than usual. Some scholars call this state a “flow state,” under which people may only be aware of activities they participate in, or of the specific environment they are in Mauri et al. (2011) . Some scholars believe that games can improve and regulate emotions, and that participants experience higher absorption after completing game tasks, thus generating more positive emotions ( Yang et al., 2020 ) and stimulating more powerful motivations ( Silic and Lowry, 2020 ). Therefore, consumers’ absorption in a game may have a positive influence on their enjoyment. Players who are obsessed with a game may have more enjoyment intentions. Therefore, we have developed our fourth hypothesis as follows:

H4: Absorption of gamification has a positive impact on the generation of enjoyment.

Gamification by nature thrives in the context of competition to win ( Morschheuser et al., 2016 ; Mitchell et al., 2020 ). People challenge each other to achieve the best results. Leaderboards can show game results and celebrate the winners. The basic property of games, no matter whether they are multi-player games, single-player games, or other single-user experiences, is to compete for a specific goal. When participants need to present themselves as active solutions on a competitive platform, they will actively pay more attention to participating in the gamified environment ( Deng et al., 2016 ). Consumers immerse themselves in games through the competitive environment designed by gamification. The satisfaction arising from competition with others is able to enhance the consumer’s intrinsic motivation and enjoyment of online shopping. This is because people get satisfaction from comparing themselves with others. The literature on CET indicates that individuals are motivated to achieve better results in competition ( Ryan and Deci, 2000a , b ) and to obtain a better experience of enjoyment. Therefore, we propose the following hypothesis:

H5: Competition of gamification has a positive impact on the generation of enjoyment.

This study extends CET by identifying the antecedents of need satisfaction, and it develops a research model to explain consumer enjoyment with gamification, as shown in Figure 1 .

Figure 1. Research model.

Methodology

Taobao, China’s largest online shopping website, has 576 million users. In November 2019, Taobao launched a game called “Stackopolis.” In this game, consumers can get rewards or discounts, and a large number of consumers have played the game. We adopted a questionnaire survey to test our hypotheses. Given that our model covers different constructs, such as consumer absorption in games and self-control, we used a structural equation model to discover through path analysis whether the relationship between these variables is statistically significant ( Deci, 1985 ). The method used to develop measurement items and collect data is discussed in more detail in this section.

The data was collected from Chinese consumers who shopped on Taobao in November 2019. We conducted a survey using purposive sampling. Taobao was selected as the subject of the case study because as an online shopping website it is second only to Amazon in the world, which means that it allows for sufficiently representative sampling required to discuss the impact of gamification on consumer purchase intention. To improve the response rate, we offered each participant RMB 20 once they had completed the questionnaire. A total of 350 questionnaires were collected. After the questionnaires were checked, 28 questionnaires were omitted as invalid. The number of valid questionnaires was 322. The main targets for data collection were consumers between the ages of 20 and 40, as they are the biggest consumer groups in the online shopping market. The information about the sample profile is shown in Table 2 .

Table 2. Descriptive statistics.

When self-report questionnaires are used to collect data at the same time from the same participants, common method variance (CMV) may be a concern. A post hoc Harman one-factor analysis was used to test for common method variance ( Podsakoff and Organ, 1986 ). The explained variance in one factor is 38.54%, which is smaller than the recommended threshold of 50%. Therefore, CMB is not problematic in this study ( Harman, 1976 ).

This is a cross-sectional study whose research framework and survey instrument have been approved by the Institutional Review Board of National Kaohsiung University of Science and Technology. The researchers contacted the consumers who were willing to receive the questionnaire by email first. Each survey package contained a covering letter explaining the purpose of the survey and the survey instrument. Before filling out the questionnaires, consumers were asked to understand the right of attending survey to ensure research ethical aspects.

A questionnaire survey was used to collect data and develop measurement items using a five-point Likert-type scale, in which “1” means “strongly disagree” and “5” means “strongly agree.” The English questionnaire was translated into Chinese by a researcher whose first language is Chinese, and the Chinese questionnaire was translated into English by another researcher to ensure that the meaning of items did not change because of translation. Afterward, the questionnaire was sent to six consumers who had experience in bilingual online shopping to further check the accuracy of the translation and the clarity of the questionnaire, and then some expressions were adjusted on the basis of their feedback.

Items of enjoyment were adopted from Sykes et al. (2009) to Kim et al. (2013) , and we adopted items for autonomy from Sheldon et al. (2001) to Jang et al. (2009) . Items for rewards were adopted from Kankanhalli et al. (2005) ; Sen et al. (2008) , O’Brien (2010); Wakefield et al. (2011) . Items for competition were adopted from Chen et al. (1998) ; Ma and Agarwal (2007) , Lee and Yang (2011) , and items for absorption from Schaufeli et al. (2002) . Finally, we adopted items for online purchase intention from Huang et al. (2017) . In the scale of purchase intention, VIP service can be referred as offering consumers a very individual form of online shopping. We collected the data by means of a questionnaire (see Table A1 ).

Data Analysis Strategy

The hypotheses of research framework have been tested and paths have been included via structural equation modeling in this study. Measurement model was performed using IBM-SPSS 25 and SmartPLS 3.0 statistical program; Partial least squares structural equation modeling (PLS-SEM) was adopted to construct the structural model, specifically, verification of the structural model was performed using SmartPLS 3.0 (path analysis).

Results and Analysis

Measurement model.

A two-stage analytical procedure was used for the data analysis ( Deterding et al., 2011 ). The measurement model for reliability and validity was assessed in the first stage, and the structural model was examined in the second stage to test the hypotheses ( Hair et al., 1998 ).

Confirmatory factor analysis (CFA) for latent variables of Smart-PLS 3.0 and SPSS 25 were used as the analytical tools for this study. All factors have strong significance, so the intrinsic consistency and convergent validity of each scale are supported, indicating that the structure is sufficiently reliable ( Hair et al., 1998 ; Table 3 ).

Table 3. Validity and correlation of constructs.

We have examined the average variance extracted (AVE) in order to assess discriminant validity. If the AVE from a construct is greater than the variance shared between the construct and the other constructs in the model, a satisfactory discriminant validity is obtained ( Chin, 1998 ). The square root of the AVE of each construct should exceed its correlation with all the other constructs. It can be seen from Table 3 that the AVE for each construct is larger than its correlation with all the other constructs in the model, which ensures the discriminant validity of the constructs.

Henseler et al. (2015) proposed the heterotrait–monotrait (HTMT) ratio of the correlations. Henseler et al. (2015) suggested 0.90 as a threshold value for structural models with dimensions. In this study, the values ranged from 0.100 to 0.746, which indicated that discriminate validity was established for all dimensions of the model, as shown in Table 4 .

Table 4. Discriminant validity: Heterotrsait–monotrait (HTMT).

Testing Structural Model Fit

Before proceeding to test the model, we first tested model fit by using three model fitting parameters: the standardized root mean square residual (SRMR), the normed fit index (NFI) and the exact model fit (bootstrap-based statistical inference). Henseler et al. (2015) introduced the SRMR as a goodness-of-fit measure for PLS-SEM that can be used to avoid model misspecification. NFI values above 0.9 usually represent acceptable fit. The third fit value is exact model fit, which tests the statistical (bootstrap-based) inference of the discrepancy between the empirical covariance matrix and the covariance matrix implied by the composite factor model. Dijkstra and Henseler et al. (2015) suggested the d_LS (i.e., the squared Euclidean distance) and the d_G (i.e., the geodesic distance) as two different ways to compute this discrepancy. A model fits well if the difference between the correlation matrix implied by the model being tested and the empirical correlation matrix is so small that it can be purely attributed to sampling error, thus the difference between the correlation matrix implied by your model and the empirical correlation matrix should be non-significant ( p > 0.05). Henseler et al. (2015) considered that d ULS and d G are smaller than the 95% bootstrapped quantile (HI 95% of d ULS and HI 95% of d G ).

In this study, the SRMR value is 0.055 (<0.08) and the NFI is 0.912 (>0.90) and the d ULS < bootstrapped HI 95% of d ULS and d G < bootstrapped HI 95% of d G , indicating the data fits the model well.

Inner Model Analysis

To assess the structural model, Hair et al. (2017) suggested looking at the R 2 , beta (β) and the corresponding t-values via a bootstrapping procedure with a resample of 5,000. They also suggested that in addition to these basic measures, researchers should also report the predictive relevance (Q 2 ), as well as the effect sizes (f 2 ). As asserted by Sullivan and Feinn (2012) , while a p -Value can inform the reader whether an effect exists, it will not reveal the size of the effect. In reporting and interpreting studies, both the substantive significance (effect size) and statistical significance ( p -Value) are essential results to be reported (p. 279). Hahn and Ang (2017) summarized some of the recommended rigor in reporting results in quantitative studies, which includes the use of replication studies, the use of effect size estimates and confidence intervals, the use of Bayesian methods, Bayes factors or likelihood ratios, and decision-theoretic modeling. Prior to hypotheses testing, the values of the variance inflation factor (VIF) have been determined. The VIF values are less than 5, ranging from 1.000 to 2.132. Thus, there have been no collinearity issues among the predictor latent variables ( Hair et al., 2017 ).

Figure 2 shows the test results of the structural model. The results in Table 5 show that reward has a positive impact on enjoyment (β = 0.27, p < 0.001); autonomy has a positive influence on enjoyment (β = 0.24, p < 0.001); absorption is positively correlated with enjoyment (β = 0.35, p < 0.001); and enjoyment has a positive correlation with purchase intention (β = 0.87, p < 0.001). Therefore, all hypotheses except for H5 have been supported. The Stone-Geisser Q 2 values obtained through the blindfolding procedures for enjoyment (Q 2 = 0.342) and online purchase intention (Q 2 = 0.423) are larger than zero, confirming that the model has predictive relevance ( Hair et al., 2017 ).

Figure 2. The results of PLS-SEM (*** if p < 0.001).

Table 5. Results of the paths.

Discussion and Conclusion

This study aims to explore how gamification affects consumer purchase intention. All the hypotheses except for H5 are supported, which provides powerful evidence for the model’s validity. This study shows that gamification can enhance consumer online purchase intention when game dynamics meets the psychological needs of consumers ( Mullins and Sabherwal, 2020 ). It is worth noting that different game dynamics increase consumer satisfaction in different ways. Based on the results, this study proposes several specific contributions.

First, it is found that rewards, autonomy, and absorption of gamification elements enhance consumer enjoyment, and such consumer enjoyment promotes online purchase intention. This result is consistent with the importance of intrinsic motivation in the CET model as emphasized by other scholars ( Deci and Ryan, 1980 ; Chae et al., 2017 ; Huotari and Hamari, 2017 ), according to which positive motivation and attitude of consumers can be produced via intrinsic motivation generated by gamification elements that enhance consumer enjoyment. The results also show that combining the above gamification factors satisfies the basic psychological needs of individuals, which is the key to enhancing the enjoyment in games, while the degree of enjoyment in games is the main determinant of consumer online purchase intention ( Xi and Hamari, 2020 ). This also implies that the intrinsic motivation in CET is to be generated when people’s psychological needs are satisfied.

Moreover, the study results show that the enjoyment value in developing gamification within the online shopping market will promote consumer behavior. In prior marketing literature, some studies have employed CET to discuss consumer motivation and behavior ( Webster and Ahuja, 2006 ; Sen et al., 2008 ; Seaborn and Fels, 2015 ); however, few studies have taken enjoyment as the important core intrinsic motivation, from the perspective of online marketing ( Xi and Hamari, 2020 ), to induce consumers to have a specific consumer behavior, especially in relation to the gamification of online platform consumption. Although enjoyment value can enhance consumers’ online purchase intention, it also relies on important gamified antecedents, which is the element of game designing ( Xi and Hamari, 2020 ). Games are generated when a group of different game elements are invoked by users in different environments. On this basis, we maintain that satisfaction of the basic psychological needs of consumers in the online shopping market is the key to the successful application of gamification. We also speculate that if any one of these psychological needs is ignored, the consumer enjoyment may be significantly reduced, and thus the consumer behavior may be adversely affected.

Finally, our study has found that competition has no positive effect on enjoyment. The competitive elements of a game may distract users and even lower their enjoyment ( Lee and Yang, 2011 ). This result is similar to the argument that despite gamification comprising many game elements, not all these elements can successfully attract users ( Hair et al., 1998 ; Huotari and Hamari, 2017 ). It would be impossible to attract consumers only by adding the enjoyment value through game elements without also considering how to meet the basic psychological needs of consumers. Previous studies have also held different views on the impact of competition, with some scholars suggesting that competition produces more driving force ( Ryan and Deci, 2000a , b ; Insley and Nunan, 2014 ; Mitchell et al., 2020 ). Other scholars have suggested that competition might have a negative effect on users’ psychological states when the competition is excessive or poorly designed such that it does not consider users’ characteristics ( Qiu and Benbasat, 2010 ). The present study verifies that competition does not have a positive impact on consumer enjoyment in the online marketing context; however, our analysis also reveals a positive correlation between competition and consumer enjoyment, implying that well-designed competition in gamification motivates consumers in experiencing enjoyment ( Mullins and Sabherwal, 2020 ).

In other words, the impact of each design element of gamification and the assessment of their impact on enjoyment are very important, and unreasonable design of competitive elements can reduce the degree of enjoyment.

Implications for Research

This study makes important academic contributions. First, it extends CET by determining which antecedents among rewards, autonomy, and absorption can satisfy the need for enjoyment ( Silverman, 2011 ; Santhanam et al., 2016 ; Huang et al., 2017 ). Researchers have found that CET can explain why people keep playing games ( Deterding et al., 2011 ), but few studies have examined the impact of game-related factors on consumer online purchase intention ( King et al., 2010 ). Our theoretical extensions help researchers develop their theories ( Webster and Ahuja, 2006 ; Sen et al., 2008 ; Seaborn and Fels, 2015 ) and explain that some gamification elements are able to attract consumers and thus influence consumer behavior when people’s psychological needs are satisfied.

Secondly, this study explains four game elements that promote enjoyment and purchase intention. Our work shows that the design of gamification should be such that consumers satisfy their extrinsic and intrinsic regulation (autonomy, reward, and absorption) ( Ryan and Deci, 2000a , b ) and participate in the next action with the support of intrinsic motivation ( Deci, 1985 ).

Thirdly, our conceptualization of structure and its measurement is beneficial for researchers as it enables them to more accurately monitor consumer behavior and analyze potential problems ( Chen et al., 2015 ). In order to understand the impact of gamification on consumer purchase intention, researchers need to control and measure variables ( Lee and Yang, 2011 ). To this end, and to make it more elaborate, the current work is conducive to the design of gamification.

Implications for Practice

This study contributes to the extant literature on practice in the following ways. First, it can enlighten system designers and administrators who are trying to influence consumer behavior through gamification. Secondly, through this kind of research, practitioners or designers who are trying to improve the consumer experience can provide consumers with a higher level of enjoyment, thereby establishing a closer relation with consumers. Finally, as Kotler (1973) argued, a well-designed sales environment may have an emotional impact on consumers and increase the possibility of purchasing. Therefore, companies should create an environment that has a positive emotional impact on consumers.

The results of this study show that competition has no positive effect on enjoyment. Thus, the competitive dynamics that frequently occur in gamification design do not necessarily have a positive impact on motivating consumers. The competitive mechanism does not necessarily motivate consumers to enjoy the website more and increase their purchase intention. The model also contributes to the commercial application of gamification and provides relevant guidance for online shopping platforms in developing game designs and social cues; in addition, it contributes to future research in this new field.

This study also has a social significance. Many social media apps use reward and competition strategies that are common in games to make the utilization of apps more enjoyable for consumers ( Silverman, 2011 ). Nevertheless, there is still a lack of prescriptive guidelines and design principles for successful application of gamification. The framework of this study has systematically explained how to help consumers enjoy themselves and make their online shopping more enjoyable. This, in turn, will pave the way for better gamified applications, and it will promote beneficial behaviors in the online society.

Limitations and Further Research Directions

Although this study enables a better understanding of the impact of gamification on consumer online purchase intention, the impact of gamification on consumer enjoyment may change with variations in the design purposes of gamification systems. We appeal to researchers to study our model outside the field of the online shopping market, as there will be more developments and discoveries in research on gamification and consumer online purchase intention. For example, although we have found that competition has no positive effect on enjoyment, current studies have suggested that the impact of competition might vary according to skill levels and competitive structures ( Liu et al., 2007 ). Therefore, in the future context of the development of gamification, further investigations are also required to be certain how different competitive structures affect enjoyment and online purchase intention.

The second limitation of this study is that our data may contain bias in its market selection. Because the object of this study is consumers participating in gamification on Taobao.com , such consumers may be more positive than those who are not attracted by gamification. Subsequent research could expand the research objects to people who are not sensitive to gamification.

We believe that our conceptualization of gamification and our empirical tests for consumer online purchase intention will lead to scholars paying more attention to gamification. We also emphasize that relevant theories need to be referred to as a basis before formulating effective gamification design strategies.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Institutional Review Board of National Kaohsiung University of Science and Technology. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

YX, ZC, and MP contributed to the ideas of educational research, collection of data, and empirical analysis. MP, ZC, MW, YP, and YX contributed to the data analysis, design of research methods, and tables. MP, MA, and YX participated in developing a research design, writing, and interpreting the analysis. All authors contributed to the literature review and conclusion, article, and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Table A1. Measurement items.

Keywords : online shopping, gamification, cognitive evaluation theory, game dynamics, consumer enjoyment

Citation: Xu Y, Chen Z, Peng MY-P and Anser MK (2020) Enhancing Consumer Online Purchase Intention Through Gamification in China: Perspective of Cognitive Evaluation Theory. Front. Psychol. 11:581200. doi: 10.3389/fpsyg.2020.581200

Received: 08 July 2020; Accepted: 23 October 2020; Published: 23 November 2020.

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Copyright © 2020 Xu, Chen, Peng and Anser. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zhong Chen, [email protected] ; Michael Yao-Ping Peng, [email protected] ; Muhammad Khalid Anser, [email protected]

† These authors have contributed equally to this work and share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Effect of Online Review Rating on Purchase Intention

• First Online: 20 September 2023

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• A. Navitha Sulthana 6 &
• S. Vasantha 6  

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The recent adoption of Web 2.0 technology has bought an enormous change in customer buying behavior. The study proposed to find the effect of online review rating on purchase intention. The relationship between online review rating and purchase intention was investigated using primary data. The primary data was gathered through an online survey among one hundred and ninety two online buyers as respondents. The study is based on descriptive analysis. The independent variable is the customer review rating and the dependent variable is Purchase Intention. The result of the study shows that review and rating has a significant effect on purchase intention. Rating or star numeric from 5 stars to 1 star are given to any product and service through the recommendation system which has an impact on purchase decision. The implication of the study provides detailed insight for the researchers, online marketers, web retailers and online buyers.

• Customer reviews
• Online buyer
• Purchase intention
• Rating and social media

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Navitha Sulthana, A., Vasantha, S. (2023). Effect of Online Review Rating on Purchase Intention. In: Biswas, A., Semwal, V.B., Singh, D. (eds) Artificial Intelligence for Societal Issues. Intelligent Systems Reference Library, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-031-12419-8_7

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A Literature Review On Purchase Intention Factors In E-Commerce

The rapid propagation of the internet has been accompanied with a wide growth of e-commerce market. This growth was however restricted due to the avalanche of information available to users from various e-commerce websites. There is a huge challenge in the aspect of decision making most especially when a buyer has to visit quite a number of e-commerce sites to make comparisons amongst prices of different products to purchase. Nevertheless, Purchase intention has been identified as a concept which gives the service providers of e-commerce systems the indication of the actual buying behavior. Therefore, this study aims to review and analyze the factors that improve and affect e-commerce customers’ purchase intention. This subject has been rarely touched in literature and needs more focus regarding its importance for both customers and service providers. This study however highlights information on patterns in e-commerce purchase intention research by analyzing the publications over the last seven years, and establishes insights and future guidance for researchers towards improving customers purchase intention. Keywords: Purchase Intention E-commerce Literature Review

Introduction

Electronic commerce (often known as e-commerce) is defined as an online trade which includes different kinds of business activities such as banking, investment, rentals and retail shopping. E-commerce involves baying, selling products or services through an electronic medium without using any paper document ( Niranjanamurthy, Kavyashree, 2013 ). The development of e-commerce has changed the traditional way of business thinking. In traditional commerce, customers were trapped to geographical and social boundaries without the chance to compare prices and products. People need to go to the marketplace to transact which usually available in specific places and times. The potential market size is limited to the region where the customers live in. E-commerce can break the old traditional boundaries and enable the users to access high quality products easily with convenience which help them to save the time and money by viewing more products and comparing prices through different and diverse websites. The activity of e-commerce can be classified into five types according to the type of business transactions: The first one is Business to Business (B2B) which refers to sales or purchase services among firms or companies. Examples of business to business e-commerce are: Alibaba, Zebra Imaging and New Town Group. Secondly, another type is business to government e-commerce which is understood as a business model that refers to trading which involves sales of products, information or services to the government parastatals. In other words, business firms can bid on government products and government organizations can purchase the products or the services. Thirdly, consumer to consumer e-commerce (sometimes can be called as customer to customer e-commerce) refers to the direct transactions between consumers usually through a third party. Examples of consumer to consumer e-commerce are: eBay and Etsy. Fourthly, business to costumer refers to business companies selling products to individuals. An example of the business to customer e-commerce is the famous Amazon website. Finally, customer to business e-commerce is the type in which customers offer a project and the budget online and the firms bid on the project then the customer chooses the company after reviewing the bids ( Kang, Jang, & Park, 2016 ). An example of this is Elane.

Online providers are interested in understanding the factors that can improve customers’ purchase intention because of the expected profits that they will get. Purchase intention refers to the user’s plans to purchase a certain product or service. Actual user behavior can be predicted through customer intention. Customer intention provides the best determinant of a person’s behavior. This intention then helps to provide an understanding of a customer’s actual behavior ( Eid, 2011 ).

Problem Statement

Generally, identifying factors affecting IT user adoption and acceptance has been considered a very important issue most especially in the context of e-commerce systems and many theories which originally came from the field of sociology and psychology have been propounded to explain the attributes that make a prospective user to make a decision to use new technological innovations ( Venkatesh, 2012 ). In the previous studies on e-commerce adoption, it was mentioned that there is a significant relationship between a user’s attitude and his intention to buy a product or service using e-commerce platform ( Bakar, Bidin, Syuhaidi, Bakar, & Bidin, 2014 ). Nevertheless, the actual behavior can also be measured by the prospective user’s intention since user’s intention is considered a determinant of a user’s behavior. However, to fully understand the user’s actual behavior, it becomes very important to firstly focus on the user’s intention ( Eid, 2011 ).In this context, purchase intention is described as the user’s decision to buy products or services using e-commerce platforms and this attribute was considered a very crucial indicator of user IT adoption in e-commerce systems ( Mansouri et al., 2012 ).Over the last decade, researchers have studied the factors that improve e-commerce customers purchase intention to increase the service provider’s profits. In this research, the articles which were published in an academic journal on e-commerce purchase intention since 2010 were reviewed and classified.

Research Questions

To achieve the objectives of this research we propose two main research questions. The research is expected to answer these questions which we believe will help the researchers to understand the factors that can convert the e-commerce user from browser to buyer and to gain an overall understanding of the IS theories adopted in the studies. The research questions of this study are:

What are the dominant IS theories in the studies of e-commerce purchase intention context?

What are the factors that influence consumers’ purchase intention in e-commerce context?

Purpose of the Study

The main purpose of this article is to explore the studies that have been published in the context of e-commerce purchase intention in the time between 2010 to 2016 focusing on the factors that can improve the purchase intention. This study aims to understand the trends of e-commerce purchase intention research by examining the published articles, and to provide a practitioners and academics with insight and future direction.

Research Methods

The researches of e-commerce purchase intention can be organized under diverse research disciplines which includes the field of IS, IT, computer science, management and marketing. After a deep search in the literature we have noticed that there is an increasing and diverse number of research papers in this area but there is a lack of comprehensive literature review studies in e-commerce customers’ purchase intention which is rarely touched in the literature. The purpose of this study is to understand the factors that improve customers’ purchase intention in e-commerce websites by examining the published articles in some of the well-recognized electronic journals and to provide the researchers in this area with future direction on the trends in this topic. The research methodology we followed is presented in Figure 01 . Firstly, the research articles for this study were classified according to publication year and the electronic journal database where they reside. After that the research articles were categorized based on IS theory and their areas of applicability. The study was performed based on the following keyword: “e-commerce purchase intention” and “online purchase intention” in the following electronic journal databases: Science Direct, ACM Digital Library, Springer, IEEE/IEE Library and Emerald Journals. The full papers were reviewed by the authors to exclude the papers which are not related to the research topic after the agreement of the authors. The time of the published articles was between 2010 until 2016. This period is considered to represent the e-commerce purchase intention research. To investigate the factors that influence the purchase intention in e-commerce websites, the previously mentioned electronic journals were searched to provide a deep review of the literature. 203 research papers were selected from the journals and reviewed. The research methodology of this study is organized into four basic stages:

First, description of the research methodology that is used in this research.

Second, classification of the research papers of e-commerce purchase intention according to the presented criteria’s.

Third, analysis of the results of the classification process.

Fourth, presenting the conclusion and discussion of the results. Paragraph text/Figures etc.

Classification Method

The classification processes started by a deep search in the electronic databases to find and download the papers that have a related topic. After that, the authors review the papers one by one to classify each one based on the classification framework. The selected papers were analysed and categorized according to the proposed framework adopted for this study. The classification process followed the deep electronic database search. After that classification results were discussed and verified by the authors. The papers were classified regarding to the: year of publication, journal and conference or information system theory.

Classification method by the electronic resources

A total number of 203 research articles were selected from six main databases which include 72 journals and 41 conferences. Research papers proportion by the electronic resources is highlighted in Figure 02 .

Classification method by the related IS theory

The chosen classification framework consists of the related IS theory. We classified the research papers after the review stage into 17 categories of IS based theories. The classification framework is presented in Figure 03 .

The papers which were collected and reviewed were classified according to the related IS theories. The IS theories which were founded as a base of the reviewed papers were almost about 50 IS theory. The most used theories were chosen as a base of the classification framework of this research. We classified the IS theories into the following 17 IS theories: Technology Acceptance Model (TAM), Theory of Reasoned Action (TRA), Theory of Planned Behaviour (TPB), Unified Theory of Acceptance and Use of Technology (UTAUT), Extended Unified Theory of Acceptance and use of Technology (UTAUT2), Expectation confirmation Model (ECM), Stimulus–Organism–Response Model (SOR),Trust Transfer Theory, Grounded Theory, IS Success Model, Information Processing Theory, Media Richness Theory (MRT), Elaboration Likelihood Model (ELM), Signalling Theory, Social Network Theory, Flow Theory and Cognitive Fit Theory.

TAM (Technology Acceptance Model): was developed by Davis ( 1985 ) to explain users’ acceptance of the technology. According to Davis the two-main construct affects users’ acceptance of the technology are: perceived usefulness (PU) and perceived ease of use (PEOU) ( Junadi & Sfenrianto, 2015 ).

TRA (The theory of reasoned action): was developed by Ajzen & Fishbein ( 1980 ). This model suggests that customers’ behaviour is determined according to subjective norm and attitude towards the behaviour which affects directly customer’s behavioural intention ( Hasbullah et al., 2016 ).

TPB (Theory of Planned Behavior): was developed by Ajzen ( 1991 ) to predict human behavioural in various fields. According to the author there are three constructs of: attitude, subjective norm and perceived behavioural control which affects directly into intention( Sara et al., 2014 ) .

Unified Theory of Acceptance and Use of Technology (UTAUT). A model established by Venkatesh et al. (2003). Venkatesh mentioned that performance expectancy, effort expectancy, facilitating condition and social influence are determinants of prospective users IT adoption.

Unified Theory of Acceptance and Use of Technology (UTAUT2) an extension to original UTAUT model established by Venkatesh ( 2012 ).

Expectation confirmation model (ECM): this model was developed based on expectation confirmation theory to predict IS customers continues usage and customers’ satisfaction by Oliver ( 1980 ). The model contains three main constructs which are: expectations, perceived performance, and disconfirmation of beliefs ( Hozhabri et al., 2014 ).

Stimulus–Organism–Response Model (SOR): the model was proposed by Mehrabian & Russell ( 1974 ). In SOR model environmental stimuli (S) affects consumer internal states (O) and influences consumers' overall responses ( Liu et al., 2016 ).

Trust transfer theory: According to Stewart ( 2003 ) trust can be transferred to new target if this target related to trusted source ( Lee et al., 2011 ) .

The Grounded Theory method was developed by Glaser & Strauss ( 1967 )Grounded theory is considered as a research methodology which involves developing a theory through the data analysis ( Ong & Teh, 2016 ).

Information System Success Model: was developed by Petter, DeLone & McLean ( 2008 ) to measure the success of an information system. According to it, the quality of the information system affects users’ acceptance and adoption of it ( Gao, Waechter, & Bai, 2015 ).

Information processing theory: Atkinson and Shiffrin (1968) developed the stage theory which is the base of information processing theory. In this theory, human brain processes the information like the computer.

Media Richness Theory (MRT): developed by Daft & Lengel ( 1986 ). Basically, this theory is about how the organization reduces the uncertainty and equivocality by processing the information.

Elaboration likelihood Model (ELM): developed by Petty & Cacioppo( 1984 ). It is a “dual-process” theory that explains the persuasion’s process.

Signaling theory: developed by Spence ( 1973 ). The main idea behind signaling theory is about two parties and how one part conveys information to the other.

Social Network Theory: it was originated by Milgram ( 1967 ) . This theory presents the social network as nodes and ties. Nodes are the actors in the network and ties are the relationships between the actors in the network.

Flow Theory: it was proposed by Csikszentmihalyi et al. ( 1975 ). This theory describes the positive experiences of enjoyment and the positive affect on individuals.

Cognitive Fit Theory: developed by Vessey ( 1991 ), this theory describes how matching between the task and the format of the presentation will result to effective problem solving.

Research papers distribution by year of publications.

The research papers in this study were chosen according to the publication year between 2010 and 2016. According to the analysis of the selected papers, it appears that the publications related to e-commerce purchase intention increase steadily between 2011 and 2016. The research papers distribution by year of publication is highlighted in Figure 04 .

Research papers distribution by information system theory

Among the 203 papers 20 papers adopted TAM model or extended it in e-commerce purchase intention context. This shows that TAM model is still popular among researchers and it is still widely used in e-commerce studies. TPB model was adopted in 18 papers in the chosen frame work, followed by TRA model which was adopted in 17 papers. S-O-R model was used in 14 papers. Classifications of research papers according to Information system theories were highlighted in Figure 05 .

Distribution of research papers by factors affecting customers purchase intention

The main purpose of this study is to investigate the factors affects customers purchase intention in e-commerce. As this issue is very important for both researchers and service providers, this topic gained the interest of researchers from different areas. The research showed that trust plays an important role in improving customers purchase intention. Trust appeared in 29 of the selected research papers as an influence factor that can derive customers purchase intention. Attitude and satisfaction were studied as some determinants of customers purchase intention in 19,18 studies respectively. Other factors such as: perceived usefulness, perceived risk, perceived value, perceived ease of use and subjective norm were investigated as important drivers of consumers purchase intention. Figure 06 presents the distribution of research papers by factors affecting customers’ purchase intention.

With the increasing demands of e-markets and the furious competition from several rivals, firms are eager to maintain their customers by gaining their satisfaction and trust. The trust issue have been investigated in several researches in the context of e-commerce, information system, market and recommender systems fields (Nilashi et al., 2016). Trust formation theories were inspected deeply in the literature and identified by several processes such as: cognition, knowledge, transference and calculation. Mayer, Davis, & Schoorman ( 1995 ) defined the trust as the “willingness to be vulnerable”. Trust is positively associated with customers’ intention to purchase a product, transact and return to the website ( Pu & Chen, 2007 ).

Satisfaction on the other hand is defined as the pleasurable fulfillment accumulated over multiple transaction experiences, which comes from overall evaluation of the online retailer. Researchers have made great efforts to understand antecedents and consequences of customers satisfaction.

As mentioned before, TRA model was adopted in 17 papers from the surveyed papers. TRA model assumes that the actual behavior is predicted by the behavioral intention. On the other hand, it proposes that subjective norm and attitude determine the customer behavior. There is a clear relationship between consumer attitude and their intention to buy products or the services using e-commerce ( Bakar et al., 2014 ).

E-commerce purchase intention has attracted the attention of researchers and academics in the last years and as we can conclude from the results, so many journals and conferences published about the research topic in the last six years. From the deep research, we came up with the following conclusions:

Among the 203 papers, 34 research papers are based on TAM model, TRA model or TPB model. These three models are still widely used in the researches related to user’s acceptance of the technology. Therefore, it is not surprising that these three models have been widely used in the e-commerce purchase intention context.

There are several models and theories which were used and applied to explain how the user accept an online product and convert from just browsing the website to purchase from it. Among these theories and models 17 theories appeared in the papers that were chosen from the literature.

After reviewing the previous publication rates, we can conclude that the interest in e-commerce purchase intention will increase in the future. This result is directly connected with the huge expansion of e-commerce and service providers’ interest of increasing the profits by knowing the factors that lead the customers to purchase from the website.

Our research is important because of the various journals and conferences that published e-commerce purchase intention researches. E-commerce purchase intentions researches have been published in MIS journals, business journals and CS journals. However, it is expected to see more publications in the research topic in the future. Published research articles dimension by journal/conference proceedings is highlighted in Figure 07 .

The research highlighted the factors that have gained the interest of the researchers in the context of e-commerce purchase intention such as: trust, attitude, satisfaction, perceived usefulness, perceived risk, perceived value, perceived ease of use and subjective norm. Other factors such as: loyalty, emotion and e-word of mouth need more focus in the research.

This study classification framework is expected to provide researchers some insights for future research direction on e-commerce purchase intention. This research has some limitations which includes lack of time, limitations regarding researches’ year of publications and the search was limited to five top electronic databases. The results of the analysis could be different if this study was extended to cover more journals and conferences. The research was performed based on the following keyword: “e-commerce purchase intention”, “online purchase intention”. However, the results may change if different keywords like:”B2B purchase intention” or “C2C purchase intention”.

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Abumalloh, R. A., Ibrahim, O. B., Nilashi, M., & Abu-Ulbeh, W. (2018). A Literature Review On Purchase Intention Factors In E-Commerce. In M. Imran Qureshi (Ed.), Technology & Society: A Multidisciplinary Pathway for Sustainable Development, vol 40. European Proceedings of Social and Behavioural Sciences (pp. 386-398). Future Academy. https://doi.org/10.15405/epsbs.2018.05.31

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The study of the effect of online review on purchase behavior: Comparing the two research methods

International Journal of Crowd Science

ISSN : 2398-7294

Article publication date: 14 February 2020

Issue publication date: 3 March 2020

The purpose of this paper is to explain the difference and connection between the network big data analysis technology and the psychological empirical research method.

Design/methodology/approach

This study analyzed the data from laboratory setting first, then the online sales data from Taobao.com to explore how the influential factors, such as online reviews (positive vs negative mainly), risk perception (higher vs lower) and product types (experiencing vs searching), interacted on the online purchase intention or online purchase behavior.

Compared with traditional research methods, such as questionnaire and behavioral experiment, network big data analysis has significant advantages in terms of sample size, data objectivity, timeliness and ecological validity.

Originality/value

Future study may consider the strategy of using complementary methods and combining both data-driven and theory-driven approaches in research design to provide suggestions for the development of e-commence in the era of big data.

• Crowd science
• Online purchase behavior
• Psychology method

Zhang, J. , Zheng, W. and Wang, S. (2020), "The study of the effect of online review on purchase behavior: Comparing the two research methods", International Journal of Crowd Science , Vol. 4 No. 1, pp. 73-86. https://doi.org/10.1108/IJCS-10-2019-0027

Emerald Publishing Limited

Copyright © 2020, Jinghuan Zhang, Wenfeng Zheng and Shan Wang.

Published in International Journal of Crowd Science . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

The development of e-commerce and the popularity of the internet, more and more people are accustomed to online shopping, they choose to buy commodities and services what they need on online. According to a report on January 2019, 74.8 per cent of netizens use online shopping. Online purchasing has become the main form of daily consumption. In this context, the study of online consumers’ purchase behavior has become the main field of consumer behavior. According to a survey report, 97.7 per cent of consumers refer to relevant reviews before online purchase. These reviews, as feed-backs of buyers, largely affect the purchase intention or purchase behavior of potential consumers. Thus, online reviews can serve as a promising data source to predict online purchase behavior. In addition, the risk perception of online purchase (when faced a buying situation, a consumer perceives a certain degree of risk involved in choice of a particular brand and how to buy it) also affects the purchase intention or decision ( Sun et al. , 2006 ). Therefore, risk perception is also a psychological variable that affects consumer purchase behavior ( Lawrence and O’Connor, 2000 ). Of course, the impact of this information on purchase intentions will be different which also depending on the type of commodities. Consumption of some commodities is common, while others have personal characteristics, making the impact of online reviews and perceived risk different.

Psychologists often use behavioral experiments in the laboratory setting to study the influential factors of consumers’ consumption decisions. Most of the methods adopted to first propose research hypotheses under the guidance of theories or based on existing studies, and then test these hypotheses under strictly controlled experimental conditions in the laboratory. For example, manipulate the proportion of positive online reviews, risk level and commodity types to explore the purchase intentions under these conditions, and then draw causal research conclusions. The biggest advantage of psychological behavioral research in the laboratory is repeatability, can withstand repeated tests and can get causal inferences.

this kind of studies can use people’s actual purchase rather than purchase intention; and

Researchers can obtain a large amount of consumption data, without having to collect it with great effort.

At the same time, these data are real, real-time, and can be verified repeatedly. However, the conclusion is based on correlation analysis and is about the correlation between the variables. The reasons behind the inference are not clear. Meanwhile, this kind of studies are the research about the commodity rather than the persons who do the purchase.

This study attempts to use two research methods and compare the differences between them from the perspective of methodology, and hope to put forward a new method to combine theory-driven with data-driven to study crowd science, with the aim of improving efficiency of the transaction, making all parties involved in the transaction taking full advantage of the online information to meet their needs.

1.1 Behavioral experiment method in psychology

Causality can be discussed. As the experimental method controls the influence of other irrelevant factors, it can be reasonably trusted to determine whether the difference in behavior results is caused by experimental conditions (i.e. independent variables), so as to make causal inference. In view of the importance of causality in research, experimental method also occupies an important position in scientific methodology,

Repeatability and verifiability. Experimental conditions of experimental method are designed by researchers in advance, so researchers have strong initiative and control. In particular, behavioral experiments have strict experimental design requirements and implementation procedures, and detailed disclosure is also made about the selection of experimental subjects, measurement tools and methods of all indicators or variables, and specific operational procedures. Therefore, the experimental method has good repeatability and testability.

1.2 Studies about online purchase behavior in psychology

An online review is a positive, neutral or negative statement, which is created by a future, actual or former consumer about a commodity or a company, and made available to the public through the internet. A growing number of researchers begin to focus on the relationship between quality of online reviews and the purchase intention. However, existing studies have found that the purchase intention of consumers is influenced by the online reviews’ quantity, which is positively correlated with the purchase intention ( Lawrence and O’Connor, 2000 ). Consumers tend to observe the proportion of positive and negative online reviews as well. The more positive reviews lead to the stronger purchase intention ( Zheng, 2008 ). However, consumers place greater emphasis on negative information in deciding to purchase ( Senecal and Nantel, 2004 ). Negative impulses attract more attention and act as stronger stimuli than positive ones. The work shows that consumers’ intention declines when the proportion of negative online reviews about a given commodity rises. When a potential consumer is exposed to a large number of negative online reviews, a negative expectation of the commodity is formed ( Chen et al. , 2012 ). Based on the existing studies, this work will further explore the impact of online reviews (positive/neutral/negative) on purchase behavior.

There is copious commodity classifications associated with online reviews. A frequently used classification is that of search and experience commodities, which is used by researchers to evaluate consumer purchase intention ( Nelson, 1974 ). A search commodity is one where information on commodity attributes is easily obtained by consumers without having to make a purchase in advance ( Hao et al. , 2009 ). Therefore, the information obtained in a search commodity is usually objective and easily compared with other similar commodities, cameras, cell phones and computers being common examples ( Li and Ren, 2017 ). On the other hand, an experience commodity is a commodity whose attributes are difficult to obtain. Consumers frequently want to feel and experience the commodity prior to any assessment. Thus, information pertaining to these commodities is mostly subjective, and evaluations conducted are based on previous experience ( Hao et al. , 2009 ). Typical examples of experience commodities are hotels, airlines, restaurants and other services ( Lim et al. , 2016 ). Consumers behave quite differently when looking for information on these two types of commodities: they tend to seek more information on other reviews concerning an experience commodity than on a search commodity ( Schlosser, 2011 ). However, some studies have pointed out that consumers are more dependent on the information provided by online reviews when purchasing search commodities ( Brodie et al. , 2013 ). The results of previous studies on the relationship between commodity types and purchase intention are not consistent. Therefore, this study would explore how commodity types affect purchase behavior in the real online shopping context.

perceived store-opportunism risk;

perceived commodity-performance risk;

perceived financial risk;

perceived delivery risk; and

perceived privacy risk ( Yu, 2016 ).

Online risk perception of consuming refers to consumers’ perception and judgment of possible adverse consequences brought by their shopping behaviors in the process of shopping ( Yu, 2016 ). Therefore, this study aims to explore how network risk perception influences purchase behavior in the network shopping context.

1.3 Big data analyze

More than ever before, the amount of data about consumers, suppliers and commodities has been exploding in today consumer world referred as “Big Data”. In addition, more data is available for the consumers from multiple sources including social network platforms. To deal with such amount of data, a new emerging technology “Big Data Analyze” is explored and employed for analyzing consumer behaviors and searching their information needs. Consumer behavior analysis is concerned with the study of inter actions among the consumers, commodities and operations such as purchasing, saving, brand choice, etc. Moreover, consumers are no longer what they used to be. Today’s consumers have evolved beyond being merely “buyers”. So, more insights information is necessary for analyzing a consumer behavior. In this aspect, Big Data has become a central role for making data driven decision making processes. However, there is no recognized concept to define the big data ( Dodds, 1991 ). Big data is usually considered as the data set that cannot be transmitted, accessed, processed and served in an endurable time period by existing communication and network systems ( Li et al. , 2018 ). Some researchers considered big data was generated by the interaction and integration of “human, machine and object”. The typical steps involved in studying big data sets: data preprocessing, dimensionality reduction and construction of predictive models.

There is an abundance of methods that can be used to build prediction models based on large data sets, ranging from relatively sophisticated approaches, such as deep learning, neural networks, probabilistic graphical models or support vector machines, to much simpler approaches, such as linear and logistic regressions. In the explanatory approach to science, the ultimate goal is to develop a mechanistic model of the data-generating process that gives rise to the observed data.

Then, combined with psychological empirical methods, how should we view psychological research based on big data analysis technology? Unfortunately, there is still very little systematic thinking on the methodology perspective of network big data psychology.

To explain the difference and connection between the network big data analysis technology and the psychological empirical research method, this study analyzed the data from laboratory setting first, then the online sales data from Taobao.com to explore how the influential factors, such as online reviews (positive vs negative mainly), risk perception (higher vs lower) and commodity types (experiencing vs searching), interacted on the online purchase intention or online purchase behavior.

2. Empirical study

2.1 study 1. the influence of consumer reviews and commodity types on online purchase intention, 2.1.1 purpose..

The purpose of this research is to analyze the role of consumer online reviews and commodity types on purchase intention by simulating online purchase behavior from the laboratory setting.

2.1.2 Methods.

2.1.2.1 participants..

We randomly sampled 120 students from Shandong Normal University and 76.7 per cent were females. The mean age of the participants was 22.03 years (SD = 1.65).

2.1.2.2 Design.

We used 2 (Online reviews: high ratio of positive reviews/high ratio of negative reviews) × 2 (commodity types: search commodity/experience commodity) in a between-subjects design. The dependent variable is consumer purchase intention.

2.1.2.3 Material.

Four psychological researchers selected USB flash disk, earphone and sound as search commodities, and clothing, facial cleanser and shoes as experience commodities. To avoid the influence of brand, price and other factors on subjects’ perception, the experimental materials only present the positive and negative proportion of commodity reviews. Among them, the material of high ratio of positive reviews’ group presented that: supposing you want to buy a commodity, 73 per cent of consumers gave the commodity positive reviews and 27 per cent gave negative reviews, and please make your decision according to the actual situation. The material of high ratio of negative online reviews’ group presented that: supposing you want to buy a commodity, 73 per cent of consumers gave the commodity negative reviews and 27 per cent gave the commodity positive reviews, and please make your decision according to the actual situation.

2.1.2.4 Research process.

This study was carried out in a quiet context. The subjects were asked to imagine themselves in the network shopping situation and assumed that they are going to buy a commodity. Then the subjects were presented the information of commodity pictures and online reviews. After they read the experimental materials, they were asked to fill in the purchase intention scale.

We adopted the purchase intention scale modified by Ma (2011) . Participants rated the way they felt on a seven-point Likert scale ranging from 1 = very little to 7 = a great extent. Cronbach’s alpha for this scale was 0.95.

2.1.3 Results.

We performed statistical analyses with online reviews and commodity types as the independent variables, the dependent variable as consumer purchase intention. The results are shown in Table I .

The results of non-repeated measures Anova ( Table II ) shows that the main effect of online reviews was significant ( F (1,116) = 238.14, p < 0.001, η p 2 = 0.67); and the main effect of commodity types is not significant, ( F (1,116) = 0.91, p >* 0.05, η p 2 = 0.01). The interaction between online reviews and commodity types is significant positively influence purchase intention. (F (1,116) = 5.93, p < 0.05, η p 2 = 0.05).

According to the results of simple effect analysis ( Figure 1 ), there is a significant difference between the online purchase intention of search commodities and experience commodities in the context of high ratio of positive online reviews ( p < 0.05), and the purchase intention of experience commodities is significantly higher than that of search commodities. No significant difference was found in purchase intention between search commodities and experience commodities ( p >* 0.05) in high ratio of negative online reviews.

Study 1 found that online reviews and commodity types provided a significant association with online purchase intention. In the context of high ratio of positive review, the online purchase intention of experience commodities is significantly higher than that of search commodities. There is no significant difference in purchase intention between search commodities and experience commodities in the context of high ratio of negative reviews. Studies have found that risk perception is negatively correlated with consumer online purchase intention, and the higher consumer risk perception is, the lower their purchase intention will be ( Chatterjee, 2001 ; Zheng, 2008 ). Risk perception as an important psychological variable that affects consumer purchase behavior, has been widely concerned by researchers ( Lawrence and O’Connor, 2000 ). Therefore, Study 2 will focus on the impact of risk perception on online purchase intention of experience commodities.

2.2 Study 2. The influence of risk perception on online purchase intention of experience commodities

2.2.1 purpose..

Firstly, the purpose of this research was to examine the influence of online reviews on purchase intention of experience commodities by simulating online purchase behavior in a laboratory setting. Secondly, this study investigated the influence of risk perception on the relationship between online reviews and purchase intention of experience commodities.

2.2.2 Methods.

2.2.2.1 participants..

The sample consisted of 120 unrelated healthy Chinese college students from Shandong Normal University, and 69.2 per cent were females. The mean age of the participants was 21.14 years (SD = 1.69).

2.2.2.2 Design.

We used 2 (Online reviews: high ratio of positive online reviews/high ratio of negative online reviews) × 2 (Risk perception: low risk perception level/high risk perception level) in a between-subjects design. The dependent variable is consumer purchase intention.

2.2.2.3 Material.

This study chose the same clothing, facial cleanser and shoes as experience commodities as in Study 1. Three decision-making tasks are used in this study. They are described in the following four different situations: high ratio of positive online reviews × high risk perception, high ratio of positive online reviews × low risk perception, high ratio of negative online reviews × high risk perception, and high ratio of negative online reviews × low risk perception. For example:

[…] high ratio of positive online reviews × low risk perception: assuming that you want to buy this kind of facial cleanser in Taobao.com, and 73 per cent of the consumers gave high ratio of positive online reviews to this commodity. Meanwhile, they think that this store provides a good service, clear logistics tracking, which are considered as a low risk perception. Please fill in the purchase intention scale according to your actual situation.

2.2.2.4 Research process.

This study was carried out in a quiet context. The participants were randomly assigned to four different situations. The subjects were asked to imagine themselves in the network shopping situation and assumed that they were going to buy a commodity. Then the subjects were presented the information. After they read the experimental materials, they were asked to fill in the purchase intention scale.

2.2.3 Results.

This paper conducted descriptive statistical analysis with online reviews and risk perception as the independent variables, and online purchase intention as the dependent variable. The results are shown in Table III .

The results of analysis of variance for non-repeated measures ( Table IV ) shows that the main effect of online reviews was significant (F (1,116) = 399.78, p < 0.001, η p 2 = 0.78). The online purchase intention of the subjects under the condition of high ratio of positive online reviews was significantly higher than that under the condition of high ratio of negative online reviews. Then, the main effect of risk perception is significant, (F (1,116) = 25.18, p <* 0.001, η p 2 = 0.18). The online purchase intention of subjects in the low risk perception group was significantly higher than that in the higher risk perception group.

According to the results of simple effect analysis, to investigate the influence of online reviews on the online purchase intention of experimental commodities under different risk perception situations. The result shows that ( Figure 2 ), under the high ratio of positive online reviews circumstances, the online purchase intention of subjects in the low risk perception context was significantly higher than that in the higher risk perception context ( p < 0.001). Under the situation of high ratio of negative online reviews, there is no significant difference between the online purchase intention of subjects under the low risk perception context and the purchase intention of subjects under the higher risk perception context ( p = 0.30).

The results of Study 2 show that the online purchase intention of subjects in the low risk perception context is significantly higher than that in the higher risk perception context. Compared with the high ratio of negative online reviews, the risk perception has a greater impact on the purchase intention of the subjects in the high ratio of positive online review situation. When faced with commodities with high ratio of positive online reviews, the lower risk perception level also Accompany by the stronger online purchase intention, which is consistent with the research hypothesis. With the increase of the risk perception level, the online purchase intention will decrease. At the same time, there is no significant difference in the influence of risk perception level on purchase intention in the high ratio of negative online review situation. This conclusion indicates that risk perception cannot adjust the relationship between the high ratio of negative online reviews and purchase intention. When the subjects are faced with the commodities of high ratio of negative online reviews, the purchase intention will be directly affected by the high ratio of negative online reviews, but not affected by the level of risk perception.

However, in network shopping context, a new risk perception is generated, which is not appearing in traditional shopping context. The perceived risk in traditional purchase context obviously is not exactly represent the perceived risk in network shopping context. Moreover, the anonymity of shopping online evaluation also makes consumers more authentic. Therefore, it is necessary to check whether the three variables have the same results in the actual online shopping situation.

2.3 Study 3. The influence of online reviews, risk perception and commodity types on purchase behavior

2.3.1 purpose..

This study explores the influence of online reviews on purchase behavior, and the role of risk perception in the real online shopping context. It intends to analyze the main factors that affect consumer purchase behavior to better improve the sales of online commodities.

2.3.2 Methods.

2.3.2.1 procedure..

In this study, Python language was used to grasp the monthly sales volume, total number of reviews, positive online reviews, neutral online reviews, negative online reviews, logistics scores and customer service scores. All data come from 300 search commodities and 300 experience commodities on Taobao.com in December 2018. The selection of commodity types and specific content is the same as Study 1. The collected data set was sorted out, and the incomplete feedback data were deleted to obtain the data collection of 590 commodities. After assigning values to online reviews, commodity sales volume and risk perception, SPSS and MPLUS were used to analyze the data.

2.3.2.2 Variable measurement.

Online reviews : we use the proportion of positive/neutral/negative reviews to analyze the relationship among purchase behavior and each kind of online reviews. The proportion of three kinds online reviews of 590 commodities was calculated, and the full distance of three types of online reviews of all commodities was obtained. Then, values were assigned to the three types of online reviews of each commodity. The proportion of positive online reviews was assigned with 92.55 per cent-94.04 per cent as 1, 94.04-95.53 per cent as 2, 95.53 per cent-97.02 per cent as 3, 97.02 per cent-98.51 per cent as 4 and 98.51 per cent-100 per cent as 5. The proportion of neutral online reviews was assigned with 0-0.692 per cent as 1, 0.692 per cent-1.384 per cent as 2, 1.384 per cent-2.076 per cent as 3, 2.076 per cent-2.768 per cent as 4, 2.768-3.46 per cent as 5. The proportion of negative online reviews was assigned with 0-0.914 per cent as 1, 0.914 per cent-1.828 per cent as 2, 1.828 per cent-2.742 per cent as 3, 2.742 per cent-3.656 per cent ass 4, 2.656 per cent-4.57 per cent as 5.

Risk perception : we use the star ratings about logistics and services as risk perception. Star rating range from one to five stars. Low to high values are assigned 1-5 points. The risk perception score of the commodity is the average of logistics and service score of each commodity. The higher score means the lower the risk perception of the consumer.

O nline purchase behavior : we use the monthly sales volume of Taobao.com at the end of December 2018 as the measurement of consumer online purchase behavior of the commodity. Then, the sales volume of the commodity is scored according to five points: assigning 1-700 to “1”, assigning 700-1400 to “2”, assigning 1400-2100 to “3”, assigning 2100-2800 to “4”, assigning 2800-3500 to “5”.

2.3.3 Result.

2.3.3.1 descriptive statistics and bivariate correlations.

The results are shown in Table V . The positive online review is not related to the purchase behavior and risk perception. The neutral online reviews had significant negative correlation with purchase behavior and risk perception. The negative online reviews were negatively correlated with purchase behavior and risk perception. As the positive online reviews are not related to purchase behavior and risk perception, the relationship between the positive online reviews and purchase behavior will not be discussed.

2.3.3.2 The relationship between the neutral online reviews, negative online reviews and purchase behavior: the moderating effect of risk perception and commodity type.

Firstly, MPLUS is used to analyze the moderating effect of risk perception and commodity type. The results ( Table VI ) showed that risk perception significantly negatively predict purchase behavior ( β = −1.08, SE = 0.10, p < 0.001). The neutral online reviews significantly negatively predict purchase behavior ( β = −0.76, SE = 0.35, p < 0.05). Our results did not show the significant relationship between the negative online reviews and purchase behavior ( β = −1.04, SE = 0.56, p >* 0.05), and the relationship between commodity type and purchase behavior was not significant ( β = −0.35, SE = 1.57, p > 0.05). The interaction of risk perception, the neutral online reviews and the negative online reviews significantly predict purchase behavior, that is, risk perception plays a positive moderating role in the relationship between the neutral online reviews, the negative online reviews and purchase behavior ( β = 0.22, SE = 0.08, p <* 0.01; β = 0.26, SE = 0.13, p < 0.05). However, the influence of interaction items of risk perception, commodity type and the negative online reviews, the neutral online reviews on purchase behavior is not significant, so the moderating variable of commodity type is no longer analyzed.

To investigate the influence of risk perception on the relationship between the neutral online reviews, the negative online reviews and purchase behavior, our study divide risk perception into high risk perception group and low risk perception group, according to the principle of average plus or minus one standard deviation. A simple slope test was carried out to investigate the influence of the neutral online reviews, the negative online reviews on purchase behavior at different levels of risk perception. The results show ( Figure 3 ) that in the case of high risk perception, the neutral online reviews and the negative online reviews has a significant predictive effect on the purchase behavior (β= −1.81, p (0.001;β= −1.77, p (0.01) in the case of low risk perception, we did not find the significantly effect of the neutral online reviews and the negative online reviews on the prediction of purchase behavior (β = 0.28, p >* 0.05;β = 0.30, p > 0.05).

The results of Study 3 show that the positive online reward was not significantly correlated with the purchase behavior, and the neutral and negative online reviews online negatively predicted the purchase behavior of consumers. It also found that risk perception plays a positive regulating role between neutral and negative online reviews and purchase behavior. In the case of higher risk perception, neutral and negative reviews had a significant effect on the prediction of buying behavior. In the case of low risk perception, neutral and negative online reviews had no significant effect on the prediction of purchase behavior.

3. Discussion

3.1 general discussion.

In the simulation of online purchase behavior, it is found that the reviews had significant impact on the purchase intention, and the purchase intention of commodities with high ratio of positive online reviews is significantly higher than that with high ratio of negative online rewards. What is inconsistent is that the analysis of real big data information found that the positive online reward was not significantly correlated with the purchase behavior, and the neutral and negative online reviews online negatively predicted the purchase behavior of consumers. Because the default set of good reviews on the website and some measures taken by merchants to get good reviews from buyers, which leads to the low reference value of favorable comments increasingly. So, consumers focus more on the relatively true descriptions of neutral and negative reviews in the purchase process. Meanwhile, in the process of shopping online, consumers will form a preliminary impression on the commodity based on the online reviews of buyers. In the process of impression formation and evaluation, more attention is paid to the negative side ( Jiang, 2015 ). Study indicates that negative ratings carry a much stronger effect than positive ones on a buyer’s trust level ( Sparks and Browning, 2011 ). Negative online reviews are viewed as an important source of information enabling online buyers to assess the quality of commodities/services. An important function of reviews is to reduce the risk and uncertainty that online buyers perceive relating to the commodity ( Ye and Zhou, 2014 ). Therefore, negative information is more likely to receive more attention and purchase behavior will be directly affected by the neutral and negative online reviews. In psychological simulated situations, the purchase intention often as a substitute for purchasing behavior also needs to be explored. Although intentions are presumed to be an indicator of to what extent people willing to approach certain behavior and how many attempt they are trying to perform certain behavior. However, there is a considerable distance between the laboratory situation and the real online shopping context, and the laboratory atmosphere also affect the psychological performance of the subjects. Although intention has been determined as a salient predictor of actual behavior to shop online, it should be acknowledged that purchase intention does not translate into purchase action ( Mo and Li, 2015 ). Researchers should explore the influencing factors of purchase behaviors in the real online context and provide reasonable suggestions for websites and sellers to generate more consumer purchase behaviors.

3.2 Crowd science

The results of behavioral research and network data are inconsistent, which causes us to rethink. Psychological research is based on theory. The fundamental hallmark of behavioral research is repeatable and can stand the test of time. Psychological research always find a causal relationship between variables. Network analysis based on big data tracks commodities rather than individuals and their psychological activities. It is based on the correlation between variables, and the underlying reasons are not clear. Both methods have their own advantages and disadvantages. So, we need to use the method of crowd science to analyze behavior.

Crowd science combines both substantive psychological science and relevant areas of the information and computer sciences. It is a complementary method and combines both data-driven and theory-driven approaches in research to provide suggestions for the development of e-commence in the era of big data. In addition to standard training in statistics and experimental design, such training programs would require coursework in software development, online data collection, machine learning and large-scale data analytics. Only when online merchants fully analyze the features of online consumers and master the consumer psychology can they be targeted to determine the business direction and business objectives according to their respective areas of expertise. They formulate commodity strategies, pricing strategies and promotional strategies for network marketing provide online services. They can better carry out network marketing activities so that it can achieve the desired purpose. It can comply with the development trend of the network economy, and make greater contributions to the development of the company. The development of crowd science really started. It can be said that all the original things may change dramatically in the context of crowd science. Similarly, the analysis of consumer buying behavior in the context of crowd science is only just beginning. Everything is still at an exploratory stage. It is still difficult to make a conclusion as to what the future looks like. A thousand people have a thousand Hamlets. Under the influence of crowd science, everyone’s feelings are different. This is precisely what crowd science wants to achieve: the precise positioning of each consumer.

The interaction among of online reviews and commodity types on consumer purchase intention

The interaction among of online reviews and risk perception on consumer purchase intention

The moderating effect of risk perception on the neutral online reviews, the negative online reviews and purchase behavior

Descriptive statistics (M±SD)

Analysis of variance of online reviews x commodity types on purchase intention

Analysis of variance of online reviews, risk perception on purchase intention

Descriptive statistics and bivariate correlations ( N = 590)

The moderating effect of risk perception, commodity types

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The Impact of Online Reviews on Consumers’ Purchasing Decisions: Evidence From an Eye-Tracking Study

1 School of Business, Ningbo University, Ningbo, China

Premaratne Samaranayake

2 School of Business, Western Sydney University, Penrith, NSW, Australia

XiongYing Cen

Yi-chen lan, associated data.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

This study investigated the impact of online product reviews on consumers purchasing decisions by using eye-tracking. The research methodology involved (i) development of a conceptual framework of online product review and purchasing intention through the moderation role of gender and visual attention in comments, and (ii) empirical investigation into the region of interest (ROI) analysis of consumers fixation during the purchase decision process and behavioral analysis. The results showed that consumers’ attention to negative comments was significantly greater than that to positive comments, especially for female consumers. Furthermore, the study identified a significant correlation between the visual browsing behavior of consumers and their purchase intention. It also found that consumers were not able to identify false comments. The current study provides a deep understanding of the underlying mechanism of how online reviews influence shopping behavior, reveals the effect of gender on this effect for the first time and explains it from the perspective of attentional bias, which is essential for the theory of online consumer behavior. Specifically, the different effects of consumers’ attention to negative comments seem to be moderated through gender with female consumers’ attention to negative comments being significantly greater than to positive ones. These findings suggest that practitioners need to pay particular attention to negative comments and resolve them promptly through the customization of product/service information, taking into consideration consumer characteristics, including gender.

Introduction

E-commerce has grown substantially over the past years and has become increasingly important in our daily life, especially under the influence of COVID-19 recently ( Hasanat et al., 2020 ). In terms of online shopping, consumers are increasingly inclined to obtain product information from reviews. Compared with the official product information provided by the sellers, reviews are provided by other consumers who have already purchased the product via online shopping websites ( Baek et al., 2012 ). Meanwhile, there is also an increasing trend for consumers to share their shopping experiences on the network platform ( Floh et al., 2013 ). In response to these trends, a large number of studies ( Floh et al., 2013 ; Lackermair et al., 2013 ; Kang et al., 2020 ; Chen and Ku, 2021 ) have investigated the effects of online reviews on purchasing intention. These studies have yielded strong evidence of the valence intensity of online reviews on purchasing intention. Lackermair et al. (2013) , for example, showed that reviews and ratings are an important source of information for consumers. Similarly, through investigating the effects of review source and product type, Bae and Lee (2011) concluded that a review from an online community is the most credible for consumers seeking information about an established product. Since reviews are comments from consumers’ perspectives and often describe their experience using the product, it is easier for other consumers to accept them, thus assisting their decision-making process ( Mudambi and Schuff, 2010 ).

A survey conducted by Zhong-Gang et al. (2015) reveals that nearly 60% of consumers browse online product reviews at least once a week and 93% of whom believe that these online reviews help them to improve the accuracy of purchase decisions, reduce the risk of loss and affect their shopping options. When it comes to e-consumers in commercial activities on B2B and B2C platforms, 82% of the consumers read product reviews before making shopping choices, and 60% of them refer to comments every week. Research shows that 93% of consumers say online reviews will affect shopping choices, indicating that most consumers have the habit of reading online reviews regularly and rely on the comments for their purchasing decisions ( Vimaladevi and Dhanabhakaym, 2012 ).

Consumer purchasing decision after reading online comments is a psychological process combining vision and information processing. As evident from the literature, much of the research has focused on the outcome and impact of online reviews affecting purchasing decisions but has shed less light on the underlying processes that influence customer perception ( Sen and Lerman, 2007 ; Zhang et al., 2010 ; Racherla and Friske, 2013 ). While some studies have attempted to investigate the underlying processes, including how people are influenced by information around the product/service using online reviews, there is limited research on the psychological process and information processing involved in purchasing decisions. The eye-tracking method has become popular in exploring and interpreting consumer decisions making behavior and cognitive processing ( Wang and Minor, 2008 ). However, there is very limited attention to how the emotional valence and the content of comments, especially those negative comments, influence consumers’ final decisions by adopting the eye-tracking method, including a gender comparison in consumption, and to whether consumers are suspicious of false comments.

Thus, the main purpose of this research is to investigate the impact of online reviews on consumers’ purchasing decisions, from the perspective of information processing by employing the eye-tracking method. A comprehensive literature review on key themes including online reviews, the impact of online reviews on purchasing decisions, and underlying processes including the level and credibility of product review information, and processing speed/effectiveness to drive customer perceptions on online reviews, was used to identify current research gaps and establish the rationale for this research. This study simulated a network shopping scenario and conducted an eye movement experiment to capture how product reviews affect consumers purchasing behavior by collecting eye movement indicators and their behavioral datum, in order to determine whether the value of the fixation dwell time and fixation count for negative comment areas is greater than that for positive comment area and to what extent the consumers are suspicious about false comments. Visual attention by both fixation dwell time and count is considered as part of moderating effect on the relationship between the valence of comment and purchase intention, and as the basis for accommodating underlying processes.

The paper is organized as follows. The next section presents literature reviews of relevant themes, including the role of online reviews and the application of eye movement experiments in online consumer decision research. Then, the hypotheses based on the relevant theories are presented. The research methodology including data collection methods is presented subsequently. This is followed by the presentation of data analysis, results, and discussion of key findings. Finally, the impact of academic practical research and the direction of future research are discussed, respectively.

Literature Review

Online product review.

Several studies have reported on the influence of online reviews, in particular on purchasing decisions in recent times ( Zhang et al., 2014 ; Zhong-Gang et al., 2015 ; Ruiz-Mafe et al., 2018 ; Von Helversen et al., 2018 ; Guo et al., 2020 ; Kang et al., 2020 ; Wu et al., 2021 ). These studies have reported on various aspects of online reviews on consumers’ behavior, including consideration of textual factors ( Ghose and Ipeirotiss, 2010 ), the effect of the level of detail in a product review, and the level of reviewer agreement with it on the credibility of a review, and consumers’ purchase intentions for search and experience products ( Jiménez and Mendoza, 2013 ). For example, by means of text mining, Ghose and Ipeirotiss (2010) concluded that the use of product reviews is influenced by textual features, such as subjectivity, informality, readability, and linguistic accuracy. Likewise, Boardman and Mccormick (2021) found that consumer attention and behavior differ across web pages throughout the shopping journey depending on its content, function, and consumer’s goal. Furthermore, Guo et al. (2020) showed that pleasant online customer reviews lead to a higher purchase likelihood compared to unpleasant ones. They also found that perceived credibility and perceived diagnosticity have a significant influence on purchase decisions, but only in the context of unpleasant online customer reviews. These studies suggest that online product reviews will influence consumer behavior but the overall effect will be influenced by many factors.

In addition, studies have considered broader online product information (OPI), comprising both online reviews and vendor-supplied product information (VSPI), and have reported on different attempts to understand the various ways in which OPI influences consumers. For example, Kang et al. (2020) showed that VSPI adoption affected online review adoption. Lately, Chen and Ku (2021) found a positive relationship between diversified online review websites as accelerators for online impulsive buying. Furthermore, some studies have reported on other aspects of online product reviews, including the impact of online reviews on product satisfaction ( Changchit and Klaus, 2020 ), relative effects of review credibility, and review relevance on overall online product review impact ( Mumuni et al., 2020 ), functions of reviewer’s gender, reputation and emotion on the credibility of negative online product reviews ( Craciun and Moore, 2019 ) and influence of vendor cues like the brand reputation on purchasing intention ( Kaur et al., 2017 ). Recently, an investigation into the impact of online review variance of new products on consumer adoption intentions showed that product newness and review variance interact to impinge on consumers’ adoption intentions ( Wu et al., 2021 ). In particular, indulgent consumers tend to prefer incrementally new products (INPs) with high variance reviews while restrained consumers are more likely to adopt new products (RNPs) with low variance.

Emotion Valence of Online Product Review and Purchase Intention

Although numerous studies have investigated factors that may influence the effects of online review on consumer behavior, few studies have focused on consumers’ perceptions, emotions, and cognition, such as perceived review helpfulness, ease of understanding, and perceived cognitive effort. This is because these studies are mainly based on traditional self-report-based methods, such as questionnaires, interviews, and so on, which are not well equipped to measure implicit emotion and cognitive factors objectively and accurately ( Plassmann et al., 2015 ). However, emotional factors are also recognized as important in purchase intention. For example, a study on the usefulness of online film reviews showed that positive emotional tendencies, longer sentences, the degree of a mix of the greater different emotional tendencies, and distinct expressions in critics had a significant positive effect on online comments ( Yuanyuan et al., 2009 ).

Yu et al. (2010) also demonstrated that the different emotional tendencies expressed in film reviews have a significant impact on the actual box office. This means that consumer reviews contain both positive and negative emotions. Generally, positive comments tend to prompt consumers to generate emotional trust, increase confidence and trust in the product and have a strong persuasive effect. On the contrary, negative comments can reduce the generation of emotional trust and hinder consumers’ buying intentions ( Archak et al., 2010 ). This can be explained by the rational behavior hypothesis, which holds that consumers will avoid risk in shopping as much as possible. Hence, when there is poor comment information presented, consumers tend to choose not to buy the product ( Mayzlin and Chevalier, 2003 ). Furthermore, consumers generally believe that negative information is more valuable than positive information when making a judgment ( Ahluwalia et al., 2000 ). For example, a single-star rating (criticism) tends to have a greater influence on consumers’ buying tendencies than that of a five-star rating (compliment), a phenomenon known as the negative deviation.

Since consumers can access and process information quickly through various means and consumers’ emotions influence product evaluation and purchasing intention, this research set out to investigate to what extent and how the emotional valence of online product review would influence their purchase intention. Therefore, the following hypothesis was proposed:

H1 : For hedonic products, consumer purchase intention after viewing positive emotion reviews is higher than that of negative emotion ones; On the other hand, for utilitarian products, it is believed that negative comments are more useful than positive ones and have a greater impact on consumers purchase intention by and large.

It is important to investigate Hypothesis one (H1) although it seems obvious. Many online merchants pay more attention to products with negative comments and make relevant improvements to them rather than those with positive comments. Goods with positive comments can promote online consumers’ purchase intention more than those with negative comments and will bring more profits to businesses.

Sen and Lerman (2007) found that compared with the utilitarian case, readers of negative hedonic product reviews are more likely to attribute the negative opinions expressed, to the reviewer’s internal (or non-product-related) reasons, and therefore, are less likely to find the negative reviews useful. However, in the utilitarian case, readers are more likely to attribute the reviewer’s negative opinions to external (or product-related) motivations, and therefore, find negative reviews more useful than positive reviews on average. Product type moderates the effect of review valence, Therefore, Hypothesis one is based on hedonic product types, such as fiction books.

Guo et al. (2020) found pleasant online customer reviews to lead to a higher purchase likelihood than unpleasant ones. This confirms hypothesis one from another side. The product selected in our experiment is a mobile phone, which is not only a utilitarian product but also a hedonic one. It can be used to make a phone call or watch videos, depending on the user’s demands.

Eye-Tracking, Online Product Review, and Purchase Intention

The eye-tracking method is commonly used in cognitive psychology research. Many researchers are calling for the use of neurobiological, neurocognitive, and physiological approaches to advance information system research ( Pavlou and Dimoka, 2010 ; Liu et al., 2011 ; Song et al., 2017 ). Several studies have been conducted to explore consumers’ online behavior by using eye-tracking. For example, using the eye-tracking method, Luan et al. (2016) found that when searching for products, customers’ attention to attribute-based evaluation is significantly longer than that of experience-based evaluation, while there is no significant difference for the experiential products. Moreover, their results indicated eye-tracking indexes, for example, fixation dwell time, could intuitively reflect consumers’ search behavior when they attend to the reviews. Also, Hong et al. (2017) confirmed that female consumers pay more attention to picture comments when they buy experience goods; when they buy searched products, they are more focused on the pure text comments. When the price and comment clues are consistent, consumers’ purchase rates significantly improve.

Eye-tracking method to explore and interpret consumers’ decision-making behavior and cognitive processing is primarily based on the eye-mind hypothesis proposed by Just and Carpenter (1992) . Just and Carpenter (1992) stated that when an individual is looking, he or she is currently perceiving, thinking about, or attending to something, and his or her cognitive processing can be identified by tracking eye movement. Several studies on consumers’ decision-making behavior have adopted the eye-tracking approach to quantify consumers’ visual attention, from various perspectives including determining how specific visual features of the shopping website influenced their attitudes and reflected their cognitive processes ( Renshaw et al., 2004 ), exploring gender differences in visual attention and shopping attitudes ( Hwang and Lee, 2018 ), investigating how employing human brands affects consumers decision quality ( Chae and Lee, 2013 ), consumer attention and different behavior depending on website content, functions and consumers goals ( Boardman and McCormick, 2019 ). Measuring the attention to the website and time spent on each purchasing task in different product categories shows that shoppers attend to more areas of the website for purposes of website exploration than for performing purchase tasks. The most complex and time-consuming task for shoppers is the assessment of purchase options ( Cortinas et al., 2019 ). Several studies have investigated fashion retail websites using the eye-tracking method and addressed various research questions, including how consumers interact with product presentation features and how consumers use smartphones for fashion shopping ( Tupikovskaja-Omovie and Tyler, 2021 ). Yet, these studies considered users without consideration of user categories, particularly gender. Since this research is to explore consumers’ decision-making behavior and the effects of gender on visual attention, the eye-tracking approach was employed as part of the overall approach of this research project. Based on existing studies, it could be that consumers may pay more attention to negative evaluations, will experience cognitive conflict when there are contradictory false comments presented, and will be unable to judge good or bad ( Cui et al., 2012 ). Therefore, the following hypothesis was proposed:

H2 : Consumers’ purchasing intention associated with online reviews is moderated/influenced by the level of visual attention.

To test the above hypothesis, the following two hypotheses were derived, taking into consideration positive and negative review comments from H1, and visual attention associated with fixation dwell time and fixation count.

H2a : When consumers intend to purchase a product, fixation dwell time and fixation count for negative comment areas are greater than those for positive comment areas.

Furthermore, when consumers browse fake comments, they are suspicious and actively seek out relevant information to identify the authenticity of the comments, which will result in more visual attention. Therefore, H2b was proposed:

H2b : Fixation dwell time and fixation count for fake comments are greater than those for authentic comments.

When considering the effect of gender on individual information processing, some differences were noted. For example, Meyers-Levy and Sternthal (1993) put forward the selectivity hypothesis, a theory of choice hypothesis, which implies that women gather all information possible, process it in an integrative manner, and make a comprehensive comparison before making a decision, while men tend to select only partial information to process and compare according to their existing knowledge—a heuristic and selective strategy. Furthermore, for an online product review, it was also reported that gender can easily lead consumers to different perceptions of the usefulness of online word-of-mouth. For example, Zhang et al. (2014) confirmed that a mixed comment has a mediating effect on the relationship between effective trust and purchasing decisions, which is stronger in women. This means that men and women may have different ways of processing information in the context of making purchasing decisions using online reviews. To test the above proposition, the following hypothesis was proposed:

H3 : Gender factors have a significant impact on the indicators of fixation dwell time and fixation count on the area of interest (AOI). Male purchasing practices differ from those of female consumers. Male consumers’ attention to positive comments is greater than that of female ones, they are more likely than female consumers to make purchase decisions easily.

Furthermore, according to the eye-mind hypothesis, eye movements can reflect people’s cognitive processes during their decision process ( Just and Carpenter, 1980 ). Moreover, neurocognitive studies have indicated that consumers’ cognitive processing can reflect the strategy of their purchase decision-making ( Rosa, 2015 ; Yang, 2015 ). Hence, the focus on the degree of attention to different polarities and the specific content of comments can lead consumers to make different purchasing decisions. Based on the key aspects outlined and discussed above, the following hypothesis was proposed:

H4 : Attention to consumers’ comments is positively correlated with consumers’ purchasing intentions: Consumers differ in the content of comments to which they gaze according to gender factors.

Thus, the framework of the current study is shown in Figure 1 .

Conceptual framework of the study.

Materials and Methods

The research adopted an experimental approach using simulated lab environmental settings for collecting experimental data from a selected set of participants who have experience with online shopping. The setting of the task was based on guidelines for shopping provided on Taobao.com , which is the most famous and frequently used C2C platform in China. Each experiment was set with the guidelines provided and carried out for a set time. Both behavioral and eye movement data were collected during the experiment.

Participants

A total of 40 healthy participants (20 males and 20 females) with online shopping experiences were selected to participate in the experiment. The participants were screened to ensure normal or correct-to-normal vision, no color blindness or poor color perception, or other eye diseases. All participants provided their written consent before the experiment started. The study was approved by the Internal Review Board of the Academy of Neuroeconomics and Neuromanagement at Ningbo University and by the Declaration of Helsinki ( World Medical Association, 2014 ).

With standardization and small selection differences among individuals, search products can be objectively evaluated and easily compared, to effectively control the influence of individual preferences on the experimental results ( Huang et al., 2009 ). Therefore, this research focused on consumer electronics products, essential products in our life, as the experiment stimulus material. To be specific, as shown in Figure 2 , a simulated shopping scenario was presented to participants, with a product presentation designed in a way that products are shown on Taobao.com . Figure 2 includes two segments: One shows mobile phone information ( Figure 2A ) and the other shows comments ( Figure 2B ). Commodity description information in Figure 2A was collected from product introductions on Taobao.com , mainly presenting some parameter information about the product, such as memory size, pixels, and screen size. There was little difference in these parameters, so quality was basically at the same level across smartphones. Prices and brand information were hidden to ensure that reviews were the sole factor influencing consumer decision-making. Product review areas in Figure 2B are the AOI, presented as a double-column layout. Each panel included 10 (positive or negative) reviews taken from real online shopping evaluations, amounting to a total of 20 reviews for each product. To eliminate the impact of different locations of comments on experimental results, the positions of the positive and negative comment areas were exchanged, namely, 50% of the subjects had positive comments presented on the left and negative comments on the right, with the remaining 50% of the participants receiving the opposite set up.

Commodity information and reviews. (A) Commodity information, (B) Commodity reviews. Screenshots of Alibaba shopfront reproduced with permission of Alibaba and Shenzhen Genuine Mobile Phone Store.

A total of 12,403 product reviews were crawled through and extracted from the two most popular online shopping platforms in China (e.g., Taobao.com and JD.com ) by using GooSeeker (2015) , a web crawler tool. The retrieved reviews were then further processed. At first, brand-related, price-related, transaction-related, and prestige-related contents were removed from comments. Then, the reviews were classified in terms of appearance, memory, running speed, logistics, and so on into two categories: positive reviews and negative reviews. Furthermore, the content of the reviews was refined to retain the original intention but to meet the requirements of the experiment. In short, reviews were modified to ensure brevity, comprehensibility, and equal length, so as to avoid causing cognitive difficulties or ambiguities in semantic understanding. In the end, 80 comments were selected for the experiment: 40 positive and 40 negative reviews (one of the negative comments was a fictitious comment, formulated for the needs of the experiment). To increase the number of experiments and the accuracy of the statistical results, four sets of mobile phone products were set up. There were eight pairs of pictures in total.

Before the experiment started, subjects were asked to read the experimental guide including an overview of the experiment, an introduction of the basic requirements and precautions in the test, and details of two practice trials that were conducted. When participants were cognizant of the experimental scenario, the formal experiment was ready to begin. Participants were required to adjust their bodies to a comfortable sitting position. The 9 points correction program was used for calibration before the experiment. Only those with a deviation angle of less than 1-degree angle could enter the formal eye movement experiment. In our eye-tracking experiment, whether the participant wears glasses or not was identified as a key issue. If the optical power of the participant’s glasses exceeds 200 degrees, due to the reflective effect of the lens, the eye movement instrument will cause great errors in the recording of eye movements. In order to ensure the accuracy of the data recorded by the eye tracker, the experimenter needs to test the power of each participant’s glasses and ensure that the degree of the participant’s glasses does not exceed 200 degrees before the experiment. After drift correction of eye movements, the formal experiment began. The following prompt was presented on the screen: “you will browse four similar mobile phone products; please make your purchase decision for each mobile phone.” Participants then had 8,000 ms to browse the product information. Next, they were allowed to look at the comments image as long as required, after which they were asked to press any key on the keyboard and answer the question “are you willing to buy this cell phone?.”

In this experiment, experimental materials were displayed on a 17-inch monitor with a resolution of 1,024 × 768 pixels. Participants’ eye movements were tracked and recorded by the Eyelink 1,000 desktop eye tracker which is a precise and accurate video-based eye tracker instrument, integrating with SR Research Experiment Builder, Data Viewer, and third-party software tools, with a sampling rate of 1,000 Hz. ( Hwang and Lee, 2018 ). Data processing was conducted by the matching Data Viewer analysis tool.

The experiment flow of each trial is shown in Figure 3 . Every subject was required to complete four trials, with mobile phone style information and comment content different and randomly presented in each trial. After the experiment, a brief interview was conducted to learn about participants’ browsing behavior when they purchased the phone and collected basic information via a matching questionnaire. The whole experiment took about 15 min.

Experimental flow diagram. Screenshots of Alibaba shopfront reproduced with permission of Alibaba and Shenzhen Genuine Mobile Phone Store.

Data Analysis

Key measures of data collected from the eye-tracking experiment included fixation dwell time and fixation count. AOI is a focus area constructed according to experimental purposes and needs, where pertinent eye movement indicators are extracted. It can guarantee the precision of eye movement data, and successfully eliminate interference from other visual factors in the image. Product review areas are our AOIs, with positive comments (IA1) and negative comments (IA2) divided into two equal-sized rectangular areas.

Fixation can indicate the information acquisition process. Tracking eye fixation is the most efficient way to capture individual information from the external environment ( Hwang and Lee, 2018 ). In this study, fixation dwell time and fixation count were used to indicate users’ cognitive activity and visual attention ( Jacob and Karn, 2003 ). It can reflect the degree of digging into information and engaging in a specific situation. Generally, a more frequent fixation frequency indicates that the individual is more interested in the target resulting in the distribution of fixation points. Valuable and interesting comments attract users to pay more attention throughout the browsing process and focus on the AOIs for much longer. Since these two dependent variables (fixation dwell time and fixation count) comprised our measurement of the browsing process, comprehensive analysis can effectively measure consumers’ reactions to different review contents.

The findings are presented in each section including descriptive statistical analysis, analysis from the perspective of gender and review type using ANOVA, correlation analysis of purchasing decisions, and qualitative analysis of observations.

Descriptive Statistical Analysis

Fixation dwell time and fixation count were extracted in this study for each record. In this case, 160 valid data records were recorded from 40 participants. Each participant generated four records which corresponded to four combinations of two conditions (positive and negative) and two eye-tracking indices (fixation dwell time and fixation count). Each record represented a review comment. Table 1 shows pertinent means and standard deviations.

Results of mean and standard deviations.

It can be noted from the descriptive statistics for both fixation dwell time and fixation count that the mean of positive reviews was less than that of negative ones, suggesting that subjects spent more time on and had more interest in negative reviews. This tendency was more obvious in female subjects, indicating a role of gender.

Fixation results can be reported using a heat mapping plot to provide a more intuitive understanding. In a heat mapping plot, fixation data are displayed as different colors, which can manifest the degree of user fixation ( Wang et al., 2014 ). Red represents the highest level of fixation, followed by yellow and then green, and areas without color represent no fixation count. Figure 4 implies that participants spent more time and cognitive effort on negative reviews than positive ones, as evidenced by the wider red areas in the negative reviews. However, in order to determine whether this difference is statistically significant or not, further inferential statistical analyses were required.

Heat map of review picture.

Repeated Measures From Gender and Review Type Perspectives—Analysis of Variance

The two independent variables for this experiment were the emotional tendency of the review and gender. A preliminary ANOVA analysis was performed, respectively, on fixation dwell time and fixation count values, with gender (man vs. woman) and review type (positive vs. negative) being the between-subjects independent variables in both cases.

A significant dominant effect of review type was found for both fixation dwell time ( p 1  < 0.001) and fixation count ( p 2  < 0.001; see Table 2 ). However, no significant dominant effect of gender was identified for either fixation dwell time ( p 1  = 0.234) or fixation count ( p 2  = 0.805). These results indicated that there were significant differences in eye movement indicators between positive and negative commentary areas, which confirms Hypothesis 2a. The interaction effect between gender and comment type was significant for both fixation dwell time ( p 1  = 0.002) and fixation count ( p 2  = 0.001). Therefore, a simple-effect analysis was carried out. The effects of different comment types with fixed gender factors and different gender with fixed comment type factors on those two dependent variables (fixation dwell time and fixation count) were investigated and the results are shown in Table 3 .

Results of ANOVA analysis.

Results of simple-effect analysis.

When the subject was female, comment type had a significant dominant effect for both fixation dwell time ( p 1  < 0.001) and fixation count ( p 2  < 0.001). This indicates that female users’ attention time and cognitive level on negative comments were greater than those on positive comments. However, the dominant effect of comment type was not significant ( p 1  = 0.336 > 0.05, p 2  = 0.43 > 0.05) for men, suggesting no difference in concern about the two types of comments for men.

Similarly, when scanning positive reviews, gender had a significant dominant effect ( p 1  = 0.003 < 0.05, p 2  = 0.025 < 0.05) on both fixation dwell time and fixation count, indicating that men exerted longer focus and deeper cognitive efforts to dig out positive reviews than women. In addition, the results for fixation count showed that gender had significant dominant effects ( p 1  = 0.18 > 0.05, p 2  = 0.01 < 0.05) when browsing negative reviews, suggesting that to some extent men pay significantly less cognitive attention to negative reviews than women, which is consistent with the conclusion that men’s attention to positive comments is greater than women’s. Although the dominant effect of gender was not significant ( p 1  = 0.234 > 0.05, p 2  = 0.805 > 0.05) in repeated measures ANOVA, there was an interaction effect with review type. For a specific type of comment, gender had significant influences, because the eye movement index between men and women was different. Thus, gender plays a moderating role in the impact of comments on consumers purchasing behavior.

Correlation Analysis of Purchase Decision

Integrating eye movement and behavioral data, whether participants’ focus on positive or negative reviews is linked to their final purchasing decisions were explored. Combined with the participants’ purchase decision results, the areas with large fixation dwell time and concerns of consumers in the picture were screened out. The frequency statistics are shown in Table 4 .

Frequency statistics of purchasing decisions.

The correlation analysis between the type of comment and the decision data shows that users’ attention level on positive and negative comments was significantly correlated with the purchase decision ( p  = 0.006 < 0.05). Thus, Hypothesis H4 is supported. As shown in Table 4 above, 114 records paid more attention to negative reviews, and 70% of the participants chose not to buy mobile phones. Also, in the 101 records of not buying, 80% of the subjects paid more attention to negative comments and chose not to buy mobile phones, while more than 50% of the subjects who were more interested in positive reviews chose to buy mobile phones. These experimental results are consistent with Hypothesis H1. They suggest that consumers purchasing decisions were based on the preliminary information they gathered and were concerned about, from which we can deduce customers’ final decision results from their visual behavior. Thus, the eye movement experiment analysis in this paper has practical significance.

Furthermore, a significant correlation ( p  = 0.007 < 0.05) was found between the comments area attracting more interest and purchase decisions for women, while no significant correlation was found for men ( p  = 0.195 > 0.05). This finding is consistent with the previous conclusion that men’s attention to positive and negative comments is not significantly different. Similarly, this also explains the moderating effect of gender. This result can be explained further by the subsequent interview of each participant after the experiment was completed. It was noted from the interviews that most of the male subjects claimed that they were more concerned about the hardware parameters of the phone provided in the product information picture. Depending on whether it met expectations, their purchasing decisions were formed, and mobile phone reviews were taken as secondary references that could not completely change their minds.

Figure 5 shows an example of the relationship between visual behavior randomly selected from female participants and the correlative decision-making behavior. The English translation of words that appeared in Figure 5 is shown in Figure 4 .

Fixation count distribution.

The subjects’ fixation dwell time and fixation count for negative reviews were significantly greater than those for positive ones. Focusing on the screen and running smoothly, the female participant decided not to purchase this product. This leads to the conclusion that this subject thought a lot about the phone screen quality and running speed while selecting a mobile phone. When other consumers expressed negative criticism about these features, the female participant tended to give up buying them.

Furthermore, combined with the result of each subject’s gaze distribution map and AOI heat map, it was found that different subjects paid attention to different features of mobile phones. Subjects all had clear concerns about some features of the product. The top five mobile phone features that subjects were concerned about are listed in Table 5 . Contrary to expectations, factors, such as appearance and logistics, were no longer a priority. Consequently, the reasons why participants chose to buy or not to buy mobile phones can be inferred from the gazing distribution map recorded in the product review picture. Therefore we can provide suggestions on how to improve the design of mobile phone products for businesses according to the features that users are more concerned about.

Top 5 features of mobile phones.

Fictitious Comments Recognition Analysis

The authenticity of reviews is an important factor affecting the helpfulness of online reviews. To enhance the reputation and ratings of online stores, in the Chinese e-commerce market, more and more sellers are employing a network “water army”—a group of people who praise the shop and add many fake comments without buying any goods from the store. Combined with online comments, eye movement fixation, and information extraction theory, Song et al. (2017) found that fake praise significantly affects consumers’ judgment of the authenticity of reviews, thereby affecting consumers’ purchase intention. These fictitious comments glutted in the purchasers’ real ones are easy to mislead customers. Hence, this experiment was designed to randomly insert a fictitious comment into the remaining 79 real comments without notifying the participants in advance, to test whether potential buyers could identify the false comments and find out their impact on consumers’ purchase decisions.

The analysis of the eye movement data from 40 product review pictures containing this false commentary found that only several subjects’ visual trajectories were back and forth in this comment, and most participants exhibited no differences relative to other comments, indicating that the vast majority of users did not identify the lack of authenticity of this comment. Moreover, when asked whether they had taken note of this hidden false comment in interviews, almost 96% of the participants answered they had not. Thus, Hypothesis H2b is not supported.

This result explains why network “water armies” are so popular in China, as the consumer cannot distinguish false comments. Thus, it is necessary to standardize the e-commerce market, establish an online comment authenticity automatic identification information system, and crack down on illegal acts of employing network troops to disseminate fraudulent information.

Discussion and Conclusion

In the e-commerce market, online comments facilitate online shopping for consumers; in turn, consumers are increasingly dependent on review information to judge the quality of products and make a buying decision. Consequently, studies on the influence of online reviews on consumers’ behavior have important theoretical significance and practical implications. Using traditional empirical methodologies, such as self-report surveys, it is difficult to elucidate the effects of some variables, such as review choosing preference because they are associated with automatic or subconscious cognitive processing. In this paper, the eye-tracking experiment as a methodology was employed to test congruity hypotheses of product reviews and explore consumers’ online review search behavior by incorporating the moderating effect of gender.

Hypotheses testing results indicate that the emotional valence of online reviews has a significant influence on fixation dwell time and fixation count of AOI, suggesting that consumers exert more cognitive attention and effort on negative reviews than on positive ones. This finding is consistent with Ahluwalia et al.’s (2000) observation that negative information is more valuable than positive information when making a judgment. Specifically, consumers use comments from other users to avoid possible risks from information asymmetry ( Hong et al., 2017 ) due to the untouchability of online shopping. These findings provide the information processing evidence that customers are inclined to acquire more information for deeper thinking and to make a comparison when negative comments appear which could more likely result in choosing not to buy the product to reduce their risk. In addition, in real online shopping, consumers are accustomed to giving positive reviews as long as any dissatisfaction in the shopping process is within their tolerance limits. Furthermore, some e-sellers may be forging fake praise ( Wu et al., 2020 ). The above two phenomena exaggerate the word-of-mouth effect of negative comments, resulting in their greater effect in contrast to positive reviews; hence, consumers pay more attention to negative reviews. Thus, Hypothesis H2a is supported. However, when limited fake criticism was mixed in with a large amount of normal commentary, the subject’s eye movements did not change significantly, indicating that little cognitive conflict was produced. Consumers could not identify fake comments. Therefore, H2b is not supported.

Although the dominant effect of gender was not significant on the indicators of the fixation dwell time and fixation count, a significant interaction effect between user gender and review polarity was observed, suggesting that consumers’ gender can regulate their comment-browsing behavior. Therefore, H3 is partly supported. For female consumers, attention to negative comments was significantly greater than positive ones. Men’s attention was more homogeneous, and men paid more attention to positive comments than women. This is attributed to the fact that men and women have different risk perceptions of online shopping ( Garbarino and Strahilevitz, 2004 ). As reported in previous studies, men tend to focus more on specific, concrete information, such as the technical features of mobile phones, as the basis for their purchase decision. They have a weaker perception of the risks of online shopping than women. Women would be worried more about the various shopping risks and be more easily affected by others’ evaluations. Specifically, women considered all aspects of the available information, including the attributes of the product itself and other post-use evaluations. They tended to believe that the more comprehensive the information they considered, the lower the risk they faced of a failed purchase ( Garbarino and Strahilevitz, 2004 ; Kanungo and Jain, 2012 ). Therefore, women hope to reduce the risk of loss by drawing on as much overall information as possible because they are more likely to focus on negative reviews.

The main finding from the fixation count distribution is that consumers’ visual attention is mainly focused on reviews containing the following five mobile phone characteristics: running smoothly, battery life, fever condition of phones, pixels, and after-sales service. Considering the behavior results, when they pay more attention to negative comments, consumers tend to give up buying mobile phones. When they pay more attention to positive comments, consumers often choose to buy. Consequently, there is a significant correlation between visual attention and behavioral decision results. Thus, H4 is supported. Consumers’ decision-making intention can be reflected in the visual browsing process. In brief, the results of the eye movement experiment can be used as a basis for sellers not only to formulate marketing strategies but also to prove the feasibility and strictness of applying the eye movement tracking method to the study of consumer decision-making behavior.

Theoretical Implications

This study has focused on how online reviews affect consumer purchasing decisions by employing eye-tracking. The results contribute to the literature on consumer behavior and provide practical implications for the development of e-business markets. This study has several theoretical contributions. Firstly, it contributes to the literature related to online review valence in online shopping by tracking the visual information acquisition process underlying consumers’ purchase decisions. Although several studies have been conducted to examine the effect of online review valence, very limited research has been conducted to investigate the underlying mechanisms. Our study advances this research area by proposing visual processing models of reviews information. The findings provide useful information and guidelines on the underlying mechanism of how online reviews influence consumers’ online shopping behavior, which is essential for the theory of online consumer behavior.

Secondly, the current study offers a deeper understanding of the relationships between online review valence and gender difference by uncovering the moderating role of gender. Although previous studies have found the effect of review valence on online consumer behavior, the current study first reveals the effect of gender on this effect and explains it from the perspective of attention bias.

Finally, the current study investigated the effect of online reviews on consumer behavior from both eye-tracking and behavioral self-reports, the results are consistent with each other, which increased the credibility of the current results and also provides strong evidence of whether and how online reviews influence consumer behavior.

Implications for Practice

This study also has implications for practice. According to the analysis of experimental results and findings presented above, it is recommended that online merchants should pay particular attention to negative comments and resolve them promptly through careful analysis of negative comments and customization of product information according to consumer characteristics including gender factors. Based on the findings that consumers cannot identify false comments, it is very important to establish an online review screening system that could automatically screen untrue content in product reviews, and create a safer, reliable, and better online shopping environment for consumers.

Limitations and Future Research

Although the research makes some contributions to both theoretical and empirical literature, it still has some limitations. In the case of experiments, the number of positive and negative reviews of each mobile phone was limited to 10 positive and 10 negative reviews (20 in total) due to the size restrictions on the product review picture. The number of comments could be considered relatively small. Efforts should be made in the future to develop a dynamic experimental design where participants can flip the page automatically to increase the number of comments. Also, the research was conducted to study the impact of reviews on consumers’ purchase decisions by hiding the brand of the products. The results would be different if the brand of the products is exposed since consumers might be moderated through brand preferences and brand loyalty, which could be taken into account in future research projects.

Data Availability Statement

Author contributions.

TC conceived and designed this study. TC, PS, and MQ wrote the first draft of the manuscript. TC, XC, and MQ designed and performed related experiments, material preparation, data collection, and analysis. TC, PS, XC, and Y-CL revised the manuscript. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors wish to thank the Editor-in-Chief, Associate Editor, reviewers and typesetters for their highly constructive comments. The authors would like to thank Jia Jin and Hao Ding for assistance in experimental data collection and Jun Lei for the text-polishing of this paper. The authors thank all the researchers who graciously shared their findings with us which allowed this eye-tracking study to be more comprehensive than it would have been without their help.

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